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complete/complete_irawati_nurmala_sari.json

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+{
+  "name": "Irawati Nurmala Sari",
+  "affiliation": "Brawijaya University",
+  "email_verification": "Tidak ada email yang diverifikasi",
+  "research_interests": [
+    "Image Processing",
+    "Image Inpainting",
+    "Deep Learning"
+  ],
+  "publications_basic": [
+    {
+      "title": "Interactive image inpainting of large-scale missing region",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:_kc_bZDykSQC",
+      "index": 1,
+      "authors": "IN Sari, E Horikawa, W Du",
+      "journal_year": "IEEE Access 9, 56430-56442, 2021",
+      "year": "2021",
+      "citations": 14
+    },
+    {
+      "title": "Structure-texture consistent painting completion for artworks",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:4DMP91E08xMC",
+      "index": 2,
+      "authors": "IN Sari, W Du",
+      "journal_year": "Ieee Access 11, 27369-27381, 2023",
+      "year": "2023",
+      "citations": 7
+    },
+    {
+      "title": "Image inpainting using clustered planar structure guidance",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:4TOpqqG69KYC",
+      "index": 3,
+      "authors": "E Horikawa, IN Sari, W Du",
+      "journal_year": "Proceedings of the the 8th International Virtual Conference on Applied …, 2021",
+      "year": "2021",
+      "citations": 6
+    },
+    {
+      "title": "Pengaruh penambahan konsentrat protein ikan gabus (channa striatus) terhadap mutu kwetiau",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:L8Ckcad2t8MC",
+      "index": 4,
+      "authors": "WS Siahaan, IN Sari, S Loekman",
+      "journal_year": "JOM 1 (1), 1-13, 2015",
+      "year": "2015",
+      "citations": 6
+    },
+    {
+      "title": "Depth map estimation of single-view image using smartphone camera for a 3-dimension image generation in augmented reality",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:9ZlFYXVOiuMC",
+      "index": 5,
+      "authors": "JN Takarabe, IN Sari, W Du",
+      "journal_year": "2023 Sixth International Symposium on Computer, Consumer and Control (IS3C …, 2023",
+      "year": "2023",
+      "citations": 5
+    },
+    {
+      "title": "Image inpainting using orthogonal viewpoints and structure consistency in Manhattan World",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:M3ejUd6NZC8C",
+      "index": 6,
+      "authors": "IN Sari, Y Urano, W Du",
+      "journal_year": "Proceedings of the the 8th International Virtual Conference on Applied …, 2021",
+      "year": "2021",
+      "citations": 5
+    },
+    {
+      "title": "Rancang Bangun Penghitung Benih Ikan Menggunakan Binary Thresholding pada Raspberry Pi secara Real Time",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:ULOm3_A8WrAC",
+      "index": 7,
+      "authors": "IN Sari, VI Arinda",
+      "journal_year": "Jurnal Informatika Polinema 4 (1), 1-8, 2017",
+      "year": "2017",
+      "citations": 5
+    },
+    {
+      "title": "Rancang Bangun Aplikasi Game Edukasi Pakaian Adat Suku Batak ‘Ulos’ Pada Platform Android",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:dhFuZR0502QC",
+      "index": 8,
+      "authors": "IN Sari",
+      "journal_year": "Univ. Udayana, 1-32, 2015",
+      "year": "2015",
+      "citations": 5
+    },
+    {
+      "title": "Edge-enhanced GAN with vanishing points for image inpainting",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:aqlVkmm33-oC",
+      "index": 9,
+      "authors": "K Masaoka, IN Sari, W Du",
+      "journal_year": "2022 23rd ACIS International Summer Virtual Conference on Software …, 2022",
+      "year": "2022",
+      "citations": 4
+    },
+    {
+      "title": "Image inpainting using automatic structure propagation with auxiliary line construction",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:qxL8FJ1GzNcC",
+      "index": 10,
+      "authors": "Y Urano, IN Sari, W Du",
+      "journal_year": "2022 23rd ACIS International Summer Virtual Conference on Software …, 2022",
+      "year": "2022",
+      "citations": 4
+    },
+    {
+      "title": "Human pose tracking using online latent structured support vector machine",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:YOwf2qJgpHMC",
+      "index": 11,
+      "authors": "KL Hua, IN Sari, MC Yeh",
+      "journal_year": "MultiMedia Modeling: 23rd International Conference, MMM 2017, Reykjavik …, 2017",
+      "year": "2017",
+      "citations": 3
+    },
+    {
+      "title": "Vanishing points detection with line segments of gaussian sphere",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:Wp0gIr-vW9MC",
+      "index": 12,
+      "authors": "K Masaoka, IN Sari, W Du",
+      "journal_year": "2023 Sixth International Symposium on Computer, Consumer and Control (IS3C …, 2023",
+      "year": "2023",
+      "citations": 2
+    },
+    {
+      "title": "High-resolution art painting completion using multi-region laplacian fusion",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:mVmsd5A6BfQC",
+      "index": 13,
+      "authors": "IN Sari, K Masaoka, JN Takarabe, W Du",
+      "journal_year": "2023 Sixth International Symposium on Computer, Consumer and Control (IS3C …, 2023",
+      "year": "2023",
+      "citations": 1
+    },
+    {
+      "title": "Prompt Conditioned Batik Pattern Generation using LoRA Weighted Diffusion Model with Classifier-Free Guidance",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:ZeXyd9-uunAC",
+      "index": 14,
+      "authors": "D Izzuddin, N Yudistira, C Dewi, IN Sari, D Pradhikta",
+      "journal_year": "IEEE Access, 2024",
+      "year": "2024",
+      "citations": 0
+    },
+    {
+      "title": "Artistic Outpainting through Adaptive Image-to-Text and Text-to-Image Generation",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:7PzlFSSx8tAC",
+      "index": 15,
+      "authors": "IN Sari, R Sugahara, W Du",
+      "journal_year": "Proceedings of the 2024 10th International Conference on Computing and …, 2024",
+      "year": "2024",
+      "citations": 0
+    },
+    {
+      "title": "Weighted Similarity-Confidence Laplacian Synthesis for High-Resolution Art Painting Completion",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:QIV2ME_5wuYC",
+      "index": 16,
+      "authors": "IN Sari, W Du",
+      "journal_year": "Applied Sciences 14 (6), 2397, 2024",
+      "year": "2024",
+      "citations": 0
+    }
+  ],
+  "total_publications": 16,
+  "total_research_interests": 3,
+  "show_more_clicks": 0,
+  "extraction_successful": true,
+  "publications_detailed": [
+    {
+      "title": "Interactive image inpainting of large-scale missing region",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:_kc_bZDykSQC",
+      "index": 1,
+      "authors": "Irawati Nurmala Sari, Emiko Horikawa, Weiwei Du",
+      "journal_year": "IEEE Access 9, 56430-56442, 2021",
+      "year": "2021",
+      "citations": 14,
+      "publication_date": "2021/4/12",
+      "journal": "IEEE Access",
+      "jilid": "9",
+      "halaman": "56430-56442",
+      "penerbit": "IEEE",
+      "abstract": "Image inpainting is a challenging reconstruction of the damaged image in photography, especially for more valued artwork than before. The damages are mostly caused by scratches and worn out, so they cannot be easily fixed physically. Thus, many scientists proposed sophisticated methods for restoring the damaged image into a new one similar to an original image. However, these methods have not solved the problem effectively if the missing region is large. In this paper, we focus on how to restore a large missing region in image inpainting. This algorithm is composed of two steps: structure propagation and color propagation. In structure propagation, we segment a large region (non-homogeneous) into several small regions (homogeneous) based on the salient structure of missing region. Then, we applied a simple pixel-based inpainting method called the Fast Marching Method (FMM) to fill in the missing …",
+      "artikel_scholar": "Interactive image inpainting of large-scale missing regionIN Sari, E Horikawa, W Du - IEEE Access, 2021Dirujuk 14 kaliArtikel terkait5 versi",
+      "pdf_url": "https://ieeexplore.ieee.org/iel7/6287639/9312710/09400364.pdf"
+    },
+    {
+      "title": "Structure-texture consistent painting completion for artworks",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:4DMP91E08xMC",
+      "index": 2,
+      "authors": "Irawati Nurmala Sari, Weiwei Du",
+      "journal_year": "Ieee Access 11, 27369-27381, 2023",
+      "year": "2023",
+      "citations": 7,
+      "publication_date": "2023/3/6",
+      "journal": "Ieee Access",
+      "jilid": "11",
+      "halaman": "27369-27381",
+      "penerbit": "IEEE",
+      "abstract": "Image completion techniques have made rapid and impressive progress due to advancements in deep learning and traditional patch-based approaches. The surrounding regions of a hole played a crucial role in repairing missing areas during the restoration process. However, large holes could result in suboptimal restoration outcomes due to complex textures causing significant changes in color gradations. As a result, they led to errors such as color discrepancies, blurriness, artifacts, and unnatural colors. Additionally, recent image completion approaches focused mainly on scenery and face images with fewer textures. Given these observations, we present a structure-texture consistent completion approach for filling large holes with detailed textures. Our method focuses on improving image completion in the context of artworks, which are expressions of creativity and often have more diverse structures and …",
+      "artikel_scholar": "Structure-texture consistent painting completion for artworksIN Sari, W Du - Ieee Access, 2023Dirujuk 7 kaliArtikel terkait4 versi",
+      "pdf_url": "https://ieeexplore.ieee.org/iel7/6287639/10005208/10058918.pdf"
+    },
+    {
+      "title": "Image inpainting using clustered planar structure guidance",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:4TOpqqG69KYC",
+      "index": 3,
+      "authors": "Emiko Horikawa, Irawati Nurmala Sari, Weiwei Du",
+      "journal_year": "Proceedings of the the 8th International Virtual Conference on Applied …, 2021",
+      "year": "2021",
+      "citations": 6,
+      "publication_date": "2021/6/20",
+      "buku": "Proceedings of the the 8th International Virtual Conference on Applied Computing & Information Technology",
+      "halaman": "118-123",
+      "abstract": "This paper presents an effective method using clustered planar structure guidance for image inpainting. Our method concerns restoring the unknown area by clustering structures of related planes. It is employed to obtain precisely similar structures in the surrounding area of missing regions. The approach of our work contains four essential steps: Planar Guidance, Clustering Structures, Feature Localization, and Patch Matching. According to perspective scenes, we first extract vanishing points (vp1, vp2, and vp3) using RAndom SAmple Consensus (RANSAC) algorithm as planar guidance. Then, we cluster the structure lines of each planar into more categories using the integration between K-means++ and Elbow method. Gaussian filter and Hadamard products blend among structure categories in the feature localization. This feature position propagates the surrounding structure information into unknown areas. For …",
+      "artikel_scholar": "Image inpainting using clustered planar structure guidanceE Horikawa, IN Sari, W Du - Proceedings of the the 8th International Virtual …, 2021Dirujuk 6 kaliArtikel terkait2 versi"
+    },
+    {
+      "title": "Pengaruh penambahan konsentrat protein ikan gabus (channa striatus) terhadap mutu kwetiau",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:L8Ckcad2t8MC",
+      "index": 4,
+      "authors": "Wiwi Solvia Siahaan, IN Sari, S Loekman",
+      "journal_year": "JOM 1 (1), 1-13, 2015",
+      "year": "2015",
+      "citations": 6,
+      "publication_date": "2015",
+      "journal": "JOM",
+      "jilid": "1",
+      "terbitan": "1",
+      "halaman": "1-13",
+      "artikel_scholar": "Pengaruh penambahan konsentrat protein ikan gabus (channa striatus) terhadap mutu kwetiauWS Siahaan, IN Sari, S Loekman - JOM, 2015Dirujuk 6 kaliArtikel terkait"
+    },
+    {
+      "title": "Depth map estimation of single-view image using smartphone camera for a 3-dimension image generation in augmented reality",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:9ZlFYXVOiuMC",
+      "index": 5,
+      "authors": "Jun’Nosuke Takarabe, Irawati Nurmala Sari, Weiwei Du",
+      "journal_year": "2023 Sixth International Symposium on Computer, Consumer and Control (IS3C …, 2023",
+      "year": "2023",
+      "citations": 5,
+      "publication_date": "2023/6/30",
+      "konferensi": "2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)",
+      "halaman": "167-170",
+      "penerbit": "IEEE",
+      "abstract": "Immersive experience exhibits such as augmented reality (AR) are a way to enjoy museums and art galleries because this way not only may update information easily but also does not require physical existence. However, 3-dimension (3D) images of AR are difficult to apply with one image as depth information either is unknown, or is cost and time consuming. This paper designs a depth map estimation method from single-view image by using smartphone camera to generate a 3D image in AR. Some functions are added into AR Depth Lab [7] in a personal computer (PC). Then, the improve AR Depth Lab [7] is installed into the smartphone. The depth map can be obtained by using patch-based depth estimation [8] and saved into the smartphone. The appropriate parameters of the proposal such as patch size, the cropped image size and the appropriate image models are set by experiments.",
+      "artikel_scholar": "Depth map estimation of single-view image using smartphone camera for a 3-dimension image generation in augmented realityJN Takarabe, IN Sari, W Du - 2023 Sixth International Symposium on Computer …, 2023Dirujuk 5 kaliArtikel terkait"
+    },
+    {
+      "title": "Image inpainting using orthogonal viewpoints and structure consistency in Manhattan World",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:M3ejUd6NZC8C",
+      "index": 6,
+      "authors": "Irawati Nurmala Sari, Yuto Urano, Weiwei Du",
+      "journal_year": "Proceedings of the the 8th International Virtual Conference on Applied …, 2021",
+      "year": "2021",
+      "citations": 5,
+      "publication_date": "2021/6/20",
+      "buku": "Proceedings of the the 8th International Virtual Conference on Applied Computing & Information Technology",
+      "halaman": "93-98",
+      "abstract": "This paper proposes a fast and straightforward method for restoring a damaged hole in the Manhattan world, which has three orthogonality with one unknown camera to produce a perspective view that can foreshorten planes of similar structure. There are two essential guidance for image inpainting algorithms: Orthogonal Viewpoints and Structure Consistency. First, due to perspective view, we build orthogonal viewpoints from an image by determining the vanishing points of different planes. Second, we present structure consistency for reconstructing unknown regions of possibly foreshortened planes due to the displacement vector of repeated structures in perspective planes. For image inpainting, PatchMatch [1] algorithm handles matching structures based on two prior-primary guided methods. Our experiment results prove that our approach completes image inpainting in challenging scenes, such as perspective …",
+      "artikel_scholar": "Image inpainting using orthogonal viewpoints and structure consistency in Manhattan WorldIN Sari, Y Urano, W Du - Proceedings of the the 8th International Virtual …, 2021Dirujuk 5 kaliArtikel terkait2 versi"
+    },
+    {
+      "title": "Rancang Bangun Penghitung Benih Ikan Menggunakan Binary Thresholding pada Raspberry Pi secara Real Time",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:ULOm3_A8WrAC",
+      "index": 7,
+      "authors": "Irawati Nurmala Sari, Vivid Ichtarosa Arinda",
+      "journal_year": "Jurnal Informatika Polinema 4 (1), 1-8, 2017",
+      "year": "2017",
+      "citations": 5,
+      "publication_date": "2017/11/1",
+      "journal": "Jurnal Informatika Polinema",
+      "jilid": "4",
+      "terbitan": "1",
+      "halaman": "1-8",
+      "abstract": "Benih yang baru dipanen biasanya akan menurun kondisinya. Untuk memulihkannya ada beberapa cara salah satunya adalah menampung benih di dalam wadah penampungan sementara ketika panen dilakukan. Selain ditampung, benih juga harus dihitung untuk mengetahui jumlahnya. Perhitungan juga harus dilakukan dengan cepat dan tepat agar benih tidak menjadi lemah, lalu mati. Selama ini petani ikan masih melakukan perhitungan benih secara manual yaitu dengan menghitung satu per satu atau menggunakan volume (gelas). Sehingga selain memakan waktu yang lama, benih ikan terkadang stress dikarenakan perhitungan yang masih manual. Penelitian ini mendesain dan mengembangkan alat yang mampu menghitung benih ikan dengan mengimplementasikan pengolahan citra sebagai solusi untuk mengatasi permasalahan para petani ikan. Sistem yang dirancang dan diimplementasikan menggunakan HTML, Python, serta pengolahan citra yang menggunakan metode Thresholding, Morphology, serta pelabelan. Sistem ini diterapkan secara real time, serta dapat menghitung objek yang mendekati perhitungan yang sebenarnya. Sistem ini telah diuji menggunakan 4 data set yaitu benih yang diuji tiap kelipatan 10 dan berakhir pada pengujian 40 benih ikan. Tingkat keakuratan tertinggi mencapai 99.9977% untuk pengujian perhitungan 40 benih.",
+      "artikel_scholar": "Rancang Bangun Penghitung Benih Ikan Menggunakan Binary Thresholding pada Raspberry Pi secara Real TimeIN Sari, VI Arinda - Jurnal Informatika Polinema, 2017Dirujuk 5 kaliArtikel terkait5 versi",
+      "pdf_url": "https://jurnal.polinema.ac.id/index.php/jip/article/download/2823/2268"
+    },
+    {
+      "title": "Rancang Bangun Aplikasi Game Edukasi Pakaian Adat Suku Batak ‘Ulos’ Pada Platform Android",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:dhFuZR0502QC",
+      "index": 8,
+      "authors": "IN Sari",
+      "journal_year": "Univ. Udayana, 1-32, 2015",
+      "year": "2015",
+      "citations": 5,
+      "publication_date": "2015",
+      "journal": "Univ. Udayana",
+      "halaman": "1-32",
+      "artikel_scholar": "Rancang Bangun Aplikasi Game Edukasi Pakaian Adat Suku Batak ‘Ulos’ Pada Platform AndroidIN Sari - Univ. Udayana, 2015Dirujuk 5 kaliArtikel terkait"
+    },
+    {
+      "title": "Edge-enhanced GAN with vanishing points for image inpainting",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:aqlVkmm33-oC",
+      "index": 9,
+      "authors": "Kei Masaoka, Irawati Nurmala Sari, Weiwei Du",
+      "journal_year": "2022 23rd ACIS International Summer Virtual Conference on Software …, 2022",
+      "year": "2022",
+      "citations": 4,
+      "publication_date": "2022/7/4",
+      "konferensi": "2022 23rd ACIS International Summer Virtual Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Summer)",
+      "halaman": "113-118",
+      "penerbit": "IEEE",
+      "abstract": "Reconstructing the damaged images with perspective views has an extensive range in the field of image inpainting. However, most existing methods generated inadequately realistic restored images. Accomplishing this problem, we propose an edge-enhanced image generation model considering viewpoints. Our method applies edge map information to guide image generation based on the perspective views of an image using vanishing points detection. Texture synthesis will be presented as post-processing to complete the remaining missing regions. Experiment shows that our approach can generate perspective images with convincing details, such as indoor and outdoor facades.",
+      "artikel_scholar": "Edge-enhanced GAN with vanishing points for image inpaintingK Masaoka, IN Sari, W Du - 2022 23rd ACIS International Summer Virtual …, 2022Dirujuk 4 kaliArtikel terkait3 versi"
+    },
+    {
+      "title": "Image inpainting using automatic structure propagation with auxiliary line construction",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:qxL8FJ1GzNcC",
+      "index": 10,
+      "authors": "Yuto Urano, Irawati Nurmala Sari, Weiwei Du",
+      "journal_year": "2022 23rd ACIS International Summer Virtual Conference on Software …, 2022",
+      "year": "2022",
+      "citations": 4,
+      "publication_date": "2022/7/4",
+      "konferensi": "2022 23rd ACIS International Summer Virtual Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Summer)",
+      "halaman": "107-112",
+      "penerbit": "IEEE",
+      "abstract": "Existing image inpainting methods used traditional and deep learning methods to restore a large missing region in the damaged image. This often leads to color discrepancy and blurriness. Pre-processing of prior line detection by user assistance is usually employed to reduce the blurry of center region by segmenting the large region into more minor. However, it operates manually, which is time-consuming. This paper introduces a technique to generate two-line types: penetrator and interactor in constructing auxiliary lines as guidance. These lines assist structure propagation established automatically, while the remaining small regions are filled by texture propagation. Experiments on large regular masks demonstrate that our proposed approach generates higher-quality results than other methods.",
+      "artikel_scholar": "Image inpainting using automatic structure propagation with auxiliary line constructionY Urano, IN Sari, W Du - 2022 23rd ACIS International Summer Virtual …, 2022Dirujuk 4 kaliArtikel terkait3 versi"
+    },
+    {
+      "title": "Human pose tracking using online latent structured support vector machine",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:YOwf2qJgpHMC",
+      "index": 11,
+      "authors": "Kai-Lung Hua, Irawati Nurmala Sari, Mei-Chen Yeh",
+      "journal_year": "MultiMedia Modeling: 23rd International Conference, MMM 2017, Reykjavik …, 2017",
+      "year": "2017",
+      "citations": 3,
+      "publication_date": "2017",
+      "konferensi": "MultiMedia Modeling: 23rd International Conference, MMM 2017, Reykjavik, Iceland, January 4-6, 2017, Proceedings, Part I 23",
+      "halaman": "626-637",
+      "penerbit": "Springer International Publishing",
+      "abstract": "Tracking human poses in a video is a challenging problem and has numerous applications. The task is particularly difficult in realistic scenes because of several intrinsic and extrinsic factors, including complicated and fast movements, occlusions and lighting changes. We propose an online learning approach for tracking human poses using latent structured Support Vector Machine (SVM). The first frame in a video is used for training, in which body parts are initialized by users and tracking models are learned using latent structured SVM. The models are updated for each subsequent frame in the video sequence. To solve the occlusion problem, we formulate a Prize-Collecting Steiner tree (PCST) problem and use a branch-and-cut algorithm to refine the detection of body parts. Experiments using several challenging videos demonstrate that the proposed method outperforms two state-of-the-art methods.",
+      "artikel_scholar": "Human pose tracking using online latent structured support vector machineKL Hua, IN Sari, MC Yeh - … Modeling: 23rd International Conference, MMM 2017 …, 2017Dirujuk 3 kaliArtikel terkait3 versi"
+    },
+    {
+      "title": "Vanishing points detection with line segments of gaussian sphere",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:Wp0gIr-vW9MC",
+      "index": 12,
+      "authors": "Kei Masaoka, Irawati Nurmala Sari, Weiwei Du",
+      "journal_year": "2023 Sixth International Symposium on Computer, Consumer and Control (IS3C …, 2023",
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+      "citations": 2,
+      "publication_date": "2023/6/30",
+      "konferensi": "2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)",
+      "halaman": "48-51",
+      "penerbit": "IEEE",
+      "abstract": "Depth estimation using vanishing points is important in computer vision and has been widely used in various applications such as robotics and autonomous driving. A vanishing point is a point in the image where parallel lines appear to converge to a single point in 3D space. The detection of vanishing points in images plays a crucial role in estimating the depth of a scene. However, the accuracy of vanishing point detection is often affected by noisy or unconverged line segments detected by the line detectors. The problem with using line detectors is that they can produce noisy or unconverged line segments, leading to a decrease in the accuracy of vanishing point detection. Therefore, it is important to develop a method to extract accurate vanishing points from noisy line segments. This paper proposes an algorithm to detect vanishing points by projecting line segments to Gaussian sphere. The proposed method …",
+      "artikel_scholar": "Vanishing points detection with line segments of gaussian sphereK Masaoka, IN Sari, W Du - 2023 Sixth International Symposium on Computer …, 2023Dirujuk 2 kaliArtikel terkait2 versi"
+    },
+    {
+      "title": "High-resolution art painting completion using multi-region laplacian fusion",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:mVmsd5A6BfQC",
+      "index": 13,
+      "authors": "Irawati Nurmala Sari, Kei Masaoka, Jun’Nosuke Takarabe, Weiwei Du",
+      "journal_year": "2023 Sixth International Symposium on Computer, Consumer and Control (IS3C …, 2023",
+      "year": "2023",
+      "citations": 1,
+      "publication_date": "2023/6/30",
+      "konferensi": "2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)",
+      "halaman": "28-31",
+      "penerbit": "IEEE",
+      "abstract": "Image completion has made impressive advancements based on deep learning approaches. However, even with advanced deep learning such as Generative Adversarial Networks (GAN), the restored area is not always optimal due to small-scale texture synthesis in high resolution and inferring missing information about image content from distant contexts, resulting in distorted lines and unnatural colors, especially in art painting completion with complicated structures and textures. Although several precious art paintings have been well-preserved by curators in museums, some frequent damages such as scratches, torn-out areas, and holes are still visible and require challenging physical repairs. Therefore, for practical refinement, some researchers convert them into high-resolution digital paintings to generate crisp brush strokes, textures, shapes, and tones by assuming similarities with the original physical ones …",
+      "artikel_scholar": "High-resolution art painting completion using multi-region laplacian fusionIN Sari, K Masaoka, JN Takarabe, W Du - 2023 Sixth International Symposium on Computer …, 2023Dirujuk 1 kaliArtikel terkait"
+    },
+    {
+      "title": "Prompt Conditioned Batik Pattern Generation using LoRA Weighted Diffusion Model with Classifier-Free Guidance",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:ZeXyd9-uunAC",
+      "index": 14,
+      "authors": "Daffa Izzuddin, Novanto Yudistira, Candra Dewi, Irawati Nurmala Sari, Dyanningrum Pradhikta",
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+      "year": "2024",
+      "citations": 0,
+      "publication_date": "2024/12/27",
+      "journal": "IEEE Access",
+      "penerbit": "IEEE",
+      "abstract": "Batik, a significant element of Indonesian cultural heritage, is renowned for its intricate patterns and profound philosophical meanings. While preserving traditional batik is crucial, the creation of modern patterns is equally encouraged to keep the art form vibrant and evolving. Current research primarily focuses on batik classification, leaving a gap in the exploration of generative models for batik pattern creation. This paper investigates the application of text-to-image (T2I) generative models to synthesize batik motifs, leveraging latent diffusion models (LDM), Low-Rank Adaptation (LoRA), and classifier-free guidance. Our methodology employed a dataset of 20,000 batik images. Multimodal models such as LLaVA and BLIP were utilized to generate detailed captions for these images. A pretrained LDM was subsequently fine-tuned on its denoising U-Net part, either by naively fine-tuned the entire layer or by employing …",
+      "artikel_scholar": "Prompt Conditioned Batik Pattern Generation using LoRA Weighted Diffusion Model with Classifier-Free GuidanceD Izzuddin, N Yudistira, C Dewi, IN Sari, D Pradhikta - IEEE Access, 2024Artikel terkait",
+      "pdf_url": "https://ieeexplore.ieee.org/iel8/6287639/6514899/10817602.pdf"
+    },
+    {
+      "title": "Artistic Outpainting through Adaptive Image-to-Text and Text-to-Image Generation",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:7PzlFSSx8tAC",
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+      "authors": "Irawati Nurmala Sari, Ryuto Sugahara, Weiwei Du",
+      "journal_year": "Proceedings of the 2024 10th International Conference on Computing and …, 2024",
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+      "publication_date": "2024/4/26",
+      "buku": "Proceedings of the 2024 10th International Conference on Computing and Artificial Intelligence",
+      "halaman": "20-25",
+      "abstract": "Artistic heritage often confronts the challenges of degradation, particularly in museum environments where valuable art paintings may exhibit missing regions along their borders. This research addresses the urgent need to restore and revitalize damaged art paintings, utilizing advanced computational methods to harmoniously fill these gaps while preserving the original aesthetic. This paper introduces an innovative approach, employing adaptive Image-to-Text and Text-to-Image Generation for the completion of damaged art paintings, referred to as Artistic Outpainting. Our proposed methodology unfolds in a carefully structured three-step process. Commencing with a pixel-wise network, we employ sophisticated image inpainting techniques to restore art paintings with missing border regions, ensuring a detailed reconstruction that seamlessly integrates additional content. This sets the foundation for subsequent …",
+      "artikel_scholar": "Artistic Outpainting through Adaptive Image-to-Text and Text-to-Image GenerationIN Sari, R Sugahara, W Du - Proceedings of the 2024 10th International Conference …, 2024Artikel terkait"
+    },
+    {
+      "title": "Weighted Similarity-Confidence Laplacian Synthesis for High-Resolution Art Painting Completion",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=d4vypz8AAAAJ&citation_for_view=d4vypz8AAAAJ:QIV2ME_5wuYC",
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+      "authors": "Irawati Nurmala Sari, Weiwei Du",
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+      "penerbit": "MDPI",
+      "abstract": "Artistic image completion assumes a significant role in the preservation and restoration of invaluable art paintings, marking notable advancements through the adoption of deep learning methodologies. Despite progress, challenges persist, particularly in achieving optimal results for high-resolution paintings. The intricacies of complex structures and textures in art paintings pose difficulties for sophisticated approaches like Generative Adversarial Networks (GANs), leading to issues such as small-scale texture synthesis and the inference of missing information, resulting in distortions in lines and unnatural colors. Simultaneously, patch-based image synthesis, augmented with global optimization on the image pyramid, has evolved to enhance structural coherence and details. However, gradient-based synthesis methods face obstacles related to directionality, inconsistency, and the computational burdens associated with solving the Poisson equation in non-integrable gradient fields. This paper introduces a pioneering approach, integrating Weighted Similarity-Confidence Laplacian Synthesis to comprehensively address these challenges and advance the field of artistic image completion. Experimental results affirm the effectiveness of our approach, offering promising outcomes for the preservation and restoration of art paintings with intricate details and irregular missing regions. The integration of weighted Laplacian synthesis and patch-based completion across multi-regions ensures precise and targeted completion, outperforming existing methods. A comparative analysis underscores our method’s superiority in artifact reduction and minimizing …",
+      "artikel_scholar": "Weighted Similarity-Confidence Laplacian Synthesis for High-Resolution Art Painting CompletionIN Sari, W Du - Applied Sciences, 2024Artikel terkait8 versi"
+    }
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+}

complete/complete_rekyan_regasari_m_p.json

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+    },
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+      "title": "Clustering the potential risk of tsunami using Density-Based Spatial clustering of application with noise (DBSCAN)",
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+      "title": "Implementasi Metode Fuzzy Analytic Hierarchy Process (F-AHP) Dalam Penentuan Peminatan di MAN 2 Kota Serang",
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+    },
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+      "title": "Sistem pendukung keputusan (spk) pemilihan tanaman pangan pada suatu lahan berdasarkan kondisi tanah dengan metode promethee",
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+    },
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+      "title": "Sistem Pendukung Keputusan (SPK) Pemilihan Tanaman Pangan Berdasarkan Kondisi Tanah Menggunakan Metode ELECTRE dan TOPSIS",
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+    },
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+      "title": "Implementasi Metode K-Nearest Neighbor untuk Rekomendasi Keminatan Studi (Studi Kasus: Jurusan Teknik Informatika Universitas Brawijaya)",
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+    },
+    {
+      "title": "Sistem Pendukung Keputusan Seleksi Beasiswa PPA dan BBM Menggunakan Metode Fuzzy AHP",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:ufrVoPGSRksC",
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+      "journal_year": "STIKI Inform. J 3 (1), 2-11, 2013",
+      "year": "2013",
+      "citations": 20
+    },
+    {
+      "title": "Pengembangan Sistem Pakar Diagnosa Penyakit Sapi Potong Dengan Metode Fuzzy K-Nearest Neighbour",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:zYLM7Y9cAGgC",
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+    },
+    {
+      "title": "Pencarian Pasal Pada Kitab Undang-Undang Hukum Pidana (Kuhp) Berdasarkan Kasus Menggunakan Metode Cosine Similarity Dan Latent Semantic Indexing (Lsi)",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:UeHWp8X0CEIC",
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+      "year": "2015",
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+    },
+    {
+      "title": "Prediksi Nilai Mata Kuliah Mahasiswa Menggunakan Algoritma K-Apriori",
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+    },
+    {
+      "title": "Sistem Pendukung Keputusan Pemilihan Atlet Yang Layak Masuk Tim Pencak Silat Dengan Metode Simple Additive Weighting (SAW)",
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+    },
+    {
+      "title": "Implementasi Fuzzy Time Series Pada Prediksi Harga Daging Di Pasar Kabupaten Malang",
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+      "year": "2018",
+      "citations": 7
+    },
+    {
+      "title": "Sistem Pakar Diagnosa Penyakit Kulit Pada Anak Menggunakan Metode Certainty Factor",
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+      "authors": "D Ulya, R Regasari, MT Furqon",
+      "journal_year": "Sistem Pakar Diagnosa Penyakit Kulit Pada Anak Menggunakan Metode Certainty …, 2014",
+      "year": "2014",
+      "citations": 5
+    },
+    {
+      "title": "Sutrisno.(2017). Implementasi Metode Profile Matching untuk Seleksi Penerimaan Anggota Asisten Praktikum (Studi Kasus: Laboratorium Pembelajaran Kelompok Praktikum Basis Data …",
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+      "citations": 5,
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+    },
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+      "title": "Paralelisasi Algoritma K-Medoid Pada Gpu Menggunakan Open Cl",
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+      "authors": "MT Furqon, A Ridok, WF Mahmudy",
+      "journal_year": "Konf. Nas. Sist. Inf, 408-413, 2015",
+      "year": "2015",
+      "citations": 3
+    },
+    {
+      "title": "Sistem Identifikasi Genre Musik dengan Metode Ekstraksi Fitur FFT dan Metode Klasifikasi Linear Discriminant Analysis Beserta Similarity Measure",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:Tyk-4Ss8FVUC",
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+      "citations": 3
+    },
+    {
+      "title": "Sistem Pendukung Keputusan untuk Proses Penentuan Rumah Tangga Miskin Menggunakan Metode Weighted Product.",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:Y0pCki6q_DkC",
+      "index": 19,
+      "authors": "N Rudiarsih",
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+    },
+    {
+      "title": "Implementasi Metode Artificial Bee Colony-Kmeans (ABCKM) Untuk Pengelompokan Biji Wijen Berdasarkan Sifat Warna Cangkang Biji",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:3fE2CSJIrl8C",
+      "index": 20,
+      "authors": "E Trisnawati, R Regasari, S Sutrisno",
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+      "year": "2018",
+      "citations": 2
+    },
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+      "title": "PERBEDAAN PENGARUH METODE PENGAJARAN DIRECT DAN INDIRECT TERHADAP HASIL BELAJAR LOMPAT JAUH GAYA JONGKOK DITINJAU DARI BODY MASS INDEX",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&cstart=20&pagesize=80&citation_for_view=vcAqZLAAAAAJ:YsMSGLbcyi4C",
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+      "authors": "NS Mutaqin, M Furqon, A Kristyanto",
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+      "citations": 0
+    },
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+      "title": "AKREDITASI PROGRAM STUDI SARJANA MENGGUNAKAN METODE ANALYTIC HIERARCHY PROCESS (AHP)",
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+      "terbitan": "5",
+      "halaman": "1912-1920",
+      "abstract": "Kulit merupakan organ tubuh pada manusia yang sangat penting karena terletak pada bagian luar tubuh yang berfungsi untuk menerima rangsangan seperti sentuhan, rasa sakit dan pengaruh lainnya dari luar. Penyakit kulit salah satu penyakit yang sering dijumpai pada negara beriklim tropis seperti Indonesia. Kurangnya pengetahuan tentang jenis penyakit kulit serta tidak mengetahui cara pencegahannya mengakibatkan sesorang dapat terkena penyakit kulit tingkat akut. Sehingga dengan adanya bantuan teknologi komputer diharapkan penyakit yang menyerang kulit tubuh manusia dapat diketahui secara dini dan hal tersebut dapat memperkecil terjadinya penyakit yang lebih berbahaya. Penelitian ini bertujuan untuk menentukan klasifikasi penyakit kulit pada manusia menggunakan metode Binary Decision Tree Support Vector Machine (BDTSVM). Berdasarkan hasil pengujian didapatkan nilai akurasi terbaik sebesar 97, 14% dengan pengujian parameter SVM yaitu nilai λ(lambda)= 0, 5, C (complexity)= 1, konstanta (gamma)= 0, 01, dan itermax= 10.",
+      "artikel_scholar": "Klasifikasi penyakit kulit pada manusia menggunakan metode binary decision tree support vector machine (BDTSVM)(Studi Kasus: Puskesmas Dinoyo Kota Malang)DD Putri, MT Furqon, RS Perdana - Jurnal Pengembangan Teknologi Informasi Dan Ilmu …, 2018Dirujuk 71 kaliArtikel terkait5 versi",
+      "pdf_url": "https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/download/1425/499"
+    },
+    {
+      "title": "Implementasi Learning Vector Quantization (LVQ) untuk Klasifikasi Kualitas Air Sungai",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:eQOLeE2rZwMC",
+      "index": 2,
+      "authors": "Rifwan Hamidi, Muhammad Tanzil Furqon, Bayu Rahayudi",
+      "journal_year": "Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer 1 (12), 1758-1763, 2017",
+      "year": "2017",
+      "citations": 55,
+      "publication_date": "2017/8/4",
+      "journal": "Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer",
+      "jilid": "1",
+      "terbitan": "12",
+      "halaman": "1758-1763",
+      "abstract": "Air merupakan sumber daya alam yang sangat penting dan menjadi kebutuhan bagi aktivitas dan kelangsungan mahluk hidup, baik manusia, hewan maupun tumbuh-tumbuhan. Air sungai merupakan salah satu sumber air baku dari berbagai alternatif sumber air yang ada untuk dilakukan proses pengolahan. Namun seiring pertambahan penduduk, pertumbuhan industri, perkembangan ekonomi dan peningkatan standar hidup menyebabkan penurunan mutu atau kualitas air sungai itu sendiri. Pencemaran air sungai terjadi apabila di dalam air sungai terdapat berbagai macam zat atau kondisi yang dapat menurunkan standar kualitas air yang telah ditentukan, sehingga tidak dapat digunakan untuk kebutuhan tertentu. Oleh karena itu perlu adanya upaya untuk menjaga kualitas, kuantitas dan kontinuitas air sungai dengan melakukan pemantauan dan pengukuran kualitas air sungai. Sebelumnya telah dilakukan pengukuran dan penentuan kualitas air sungai menggunakan metode manual seperti Indeks Pencemaran (IP), Water Quality Index (WQI) dan STORET dengan kendala waktu dan biaya yang cukup tinggi. Sehingga diperlukan metode lain yang untuk mempercepat proses perhitungan secara efektif dan efisien yaitu menggunakan metode Learning Vector Quantization (LVQ) yang dapat mengklasifikasikan data menjadi 4 kelas kualitas air sungai berdasarkan 7 parameter masukan. Proses implementasi LVQ untuk klasifikasi air sungai diawali dengan tahapan pembagian dataset, pelatihan data dan pengujian serta klasifikasi data yang akan menghasilkan kelas berupa kelas memenuhi baku mutu, tercemar ringan, sedang dan berat …",
+      "artikel_scholar": "Implementasi Learning Vector Quantization (LVQ) untuk Klasifikasi Kualitas Air SungaiR Hamidi, MT Furqon, B Rahayudi - Jurnal Pengembangan Teknologi Informasi dan Ilmu …, 2017Dirujuk 55 kaliArtikel terkait3 versi",
+      "pdf_url": "http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/download/635/251"
+    },
+    {
+      "title": "Sistem pakar diagnosa penyakit sapi potong dengan metode naive bayes",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:2osOgNQ5qMEC",
+      "index": 3,
+      "authors": "Indriana Candra Dewi, Arief Andy Soebroto, Muhammad Tanzil Furqon",
+      "journal_year": "Journal of Environmental Engineering and Sustainable Technology 2 (2), 72-78, 2015",
+      "year": "2015",
+      "citations": 49,
+      "publication_date": "2015/12/16",
+      "journal": "Journal of Environmental Engineering and Sustainable Technology",
+      "jilid": "2",
+      "terbitan": "2",
+      "halaman": "72-78",
+      "abstract": "In order to produce quality beef, one of the important factors in maintenance of cattle is to maintain the health of livestock to stay fit. One way to provide an understanding of the breeders is to use expert system. An expert system is one of the artificial intelligence which is adopting of the expert knowledge that used to solve problem that usually can only be solved by expert in the field. Expert systems can be allowed to extend the working range of experts so that expert knowledge can be acquired and used anywhere. In this expert system use a Naive Bayes method as inference methods for diagnosing the disease. Types of diseases that can be recognized by expert system are 11 types of disease while symptoms that can be recognized the expert system are 20 types of symptom. The results of testing the accuracy of the 26 test case data, have generated the level of conformity percentage of 96, 15%.",
+      "artikel_scholar": "Sistem pakar diagnosa penyakit sapi potong dengan metode naive bayesIC Dewi, AA Soebroto, MT Furqon - Journal of Environmental Engineering and Sustainable …, 2015Dirujuk 49 kaliArtikel terkait7 versi",
+      "pdf_url": "https://jeest.ub.ac.id/index.php/jeest/article/download/30/60"
+    },
+    {
+      "title": "Clustering the potential risk of tsunami using Density-Based Spatial clustering of application with noise (DBSCAN)",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:d1gkVwhDpl0C",
+      "index": 4,
+      "authors": "Muhammad Tanzil Furqon, Lailil Muflikhah",
+      "journal_year": "Journal of Environmental Engineering and Sustainable Technology 3 (1), 1-8, 2016",
+      "year": "2016",
+      "citations": 48,
+      "publication_date": "2016/5/13",
+      "journal": "Journal of Environmental Engineering and Sustainable Technology",
+      "jilid": "3",
+      "terbitan": "1",
+      "halaman": "1-8",
+      "abstract": "Tsunami is one of the deadliest natural disaster that causing devastating property damage and loss of life. Therefore, this triggers many scientist to do researches in tsunami mitigation disaster, such as analyzing the potential risks caused by tsunami. The process of analyzing the potential risk caused by tsunami can be done by grouping the data of tsunami based on characteristics of the previous tsunami events. DBSCAN (Density-based Spatial Clustering of Application with Noise) is a popular clustering method and can be used to do the task. The algorithm do the clustering processes using density-based concept that able to detect outlier/noise and clusters irregular shapes. It was proved in this research where the evaluation method using Silhouette Coefficient on the DBSCAN clustering result gave highest value 0.96056649 for ε and minPts value of. 1 and 0.1.",
+      "artikel_scholar": "Clustering the potential risk of tsunami using Density-Based Spatial clustering of application with noise (DBSCAN)MT Furqon, L Muflikhah - Journal of Environmental Engineering and Sustainable …, 2016Dirujuk 48 kaliArtikel terkait7 versi",
+      "pdf_url": "https://jeest.ub.ac.id/index.php/jeest/article/viewFile/38/67"
+    },
+    {
+      "title": "Implementasi Metode Fuzzy Analytic Hierarchy Process (F-AHP) Dalam Penentuan Peminatan di MAN 2 Kota Serang",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:UebtZRa9Y70C",
+      "index": 5,
+      "authors": "Muhammad Fajri, Rekyan Regarsari Mardhi Putri, Lailil Muflikhah",
+      "journal_year": "Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer 2 (5), 2109-2117, 2018",
+      "year": "2018",
+      "citations": 44,
+      "publication_date": "2018",
+      "journal": "Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer",
+      "jilid": "2",
+      "terbitan": "5",
+      "halaman": "2109-2117",
+      "abstract": "Program peminatan diperkenalkan sebagai upaya untuk lebih mengarahkan siswa berdasarkan bakat, minat, dan kemampuan akademiknya. Terdapat empat kelompok peminatan di Madrasah Aliyah Negeri 2 Kota Serang, yaitu IPA, IPS, Bahasa, dan Agama. Peminatan IPA diperuntukkan bagi siswa yang memiliki kecenderungan dalam ilmu pasti. Peminatan IPS diperuntukkan bagi siswa yang memiliki kecenderungan ilmu sosial. Peminatan Bahasa diperuntukkan bagi siswa yang memiliki kecenderungan gemar berbahasa. Dan peminatan Agama diperuntukkan bagi siswa yang memiliki kecenderungan ilmu agama. Dalam penentuan peminatan siswa, MAN 2 Kota Serang menggunakan lima aspek peminatan diantaranya nilai penerimaan peserta didik baru (PPDB), nilai ujian nasional, nilai rapor, hasil tes psikologi, dan minta peserta didik. Namun di dalam penentuan peminatan belum ada standardisasi pembobotan dalam setiap aspek peminatan sehingga hasil yang diperoleh tidak maksimal. Fuzzy Analytical Hierarchy Process (F-AHP) sanggup mengatasi kelemahan pada kriteria yang memilki sifat subjektif lebih banyak pada metode AHP. Logika Fuzzy sendiri adalah logika yang memiliki nilai kesamaran antara dua nilai. Pada penelitian ini, akurasi yang dihasilkan adalah 76, 67% dengan 30 data uji untuk penentuan peminatan di MAN 2 Kota Serang.",
+      "artikel_scholar": "Implementasi Metode Fuzzy Analytic Hierarchy Process (F-AHP) Dalam Penentuan Peminatan di MAN 2 Kota SerangM Fajri, RRM Putri, L Muflikhah - Jurnal Pengembangan Teknologi Informasi dan Ilmu …, 2018Dirujuk 44 kaliArtikel terkait4 versi",
+      "pdf_url": "https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/download/1546/524"
+    },
+    {
+      "title": "Sistem pendukung keputusan (spk) pemilihan tanaman pangan pada suatu lahan berdasarkan kondisi tanah dengan metode promethee",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:Se3iqnhoufwC",
+      "index": 6,
+      "authors": "Wafina Nurul Adila, Rekyan Regasari, Heru Nurwasito",
+      "journal_year": "Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer 2 (5), 2118-2126, 2018",
+      "year": "2018",
+      "citations": 29,
+      "publication_date": "2018",
+      "journal": "Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer",
+      "jilid": "2",
+      "terbitan": "5",
+      "halaman": "2118-2126",
+      "abstract": "Penentuan pemilihan tanaman pangan pada suatu lahan yang sesuai ditanam berdasarkan kondisi (kriteria) lahan sangat diperlukan sebagai pendukung pengambilan keputusan. Ada 12 kriteria yang dinilai antara lain temperatur, curah hujan, kelembabpan, drainase, tekstur, kedalaman tanah, ketebalan gambut, ph h2o, salinitas, alkalinitas, kedalaman sulfidik dan lereng. Banyaknya jumlah kriteria serta tingkat kepentingan kriteria yang berbeda-beda mempersulit dalam mencapai keputusan.",
+      "artikel_scholar": "Sistem pendukung keputusan (spk) pemilihan tanaman pangan pada suatu lahan berdasarkan kondisi tanah dengan metode prometheeWN Adila, R Regasari, H Nurwasito - Jurnal Pengembangan Teknologi Informasi dan Ilmu …, 2018Dirujuk 29 kaliArtikel terkait4 versi",
+      "pdf_url": "https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/download/1555/525"
+    },
+    {
+      "title": "Sistem Pendukung Keputusan (SPK) Pemilihan Tanaman Pangan Berdasarkan Kondisi Tanah Menggunakan Metode ELECTRE dan TOPSIS",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:hqOjcs7Dif8C",
+      "index": 7,
+      "authors": "Ningsih Puji Rahayu, Rekyan Regasari Mardi Putri, Agus Wahyu Widodo",
+      "journal_year": "Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer 2 (8), 2323-2332, 2018",
+      "year": "2018",
+      "citations": 26,
+      "publication_date": "2018",
+      "journal": "Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer",
+      "jilid": "2",
+      "terbitan": "8",
+      "halaman": "2323-2332",
+      "abstract": "Negara Indonesia merupakan negara dengan letak geografis yang sangat strategis, hal ini sangat menguntungkan bagi warga negara karena hampir semua tanaman dapat ditanam di Indonesia. Terutama adalah tanaman pangan. Tanaman pangan adalah tanaman yang sangat penting untuk peran hidup makhluk hidup khususnya manusia. Diantara tanaman pangan adalah padi, jagung, kacang tanah, kedelai, keempat tanaman ini memiliki peranan yang sangat penting untuk ketahanan pangan nasional. di setiap daerah di Indonesia memiliki jenis tanah yang berbeda-beda dan pasti kecocokan untuk pertanaman berbeda juga. Dari empat tanaman pangan yaitu: padi, jagung, kacang tanah dan kedelai, akan dicocokan dengan dua belas kriteria tanah, yaitu: temperatur (c), curah hujan (mm), kelembaban (%), drainase, tekstur, kedalaman tanah (cm), ketebalan gambut (cm), ph h2o, salinitas (ds/m), alkalinitas (%), kedalaman sulfidik (cm), lereng (%). Dari 12 kriteria itu akan dicocokan dengan kondisi tanah yang ada didaerah klaten. Dengan cara mencocokan kesesuaian lahan berdasarkan kriteria tersebut maka akan lebih mempermudah petani dalam menentukan tanaman pangan apa yang cocok untuk daerah tersebut dengan begitu maka hasil pertanian akan lebih meningkat. Metode ELECTRE dan TOPSIS merupakan metode analisis pengambilan keputusan multikriteria, ELECTRE didasaran pada konsep outrangking dengan menggunakan perbandingan berpasangan dari alternatif berdasarkan setiap kriteria yang sesuai. Pada penelitian ini mengapa menggunakan metode ELECTRE karena pada metode ELECTRE sangat cocok …",
+      "artikel_scholar": "Sistem Pendukung Keputusan (SPK) Pemilihan Tanaman Pangan Berdasarkan Kondisi Tanah Menggunakan Metode ELECTRE dan TOPSISNP Rahayu, RRM Putri, AW Widodo - Jurnal Pengembangan Teknologi Informasi dan Ilmu …, 2018Dirujuk 26 kaliArtikel terkait4 versi",
+      "pdf_url": "https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/download/1743/667"
+    },
+    {
+      "title": "Implementasi Metode K-Nearest Neighbor untuk Rekomendasi Keminatan Studi (Studi Kasus: Jurusan Teknik Informatika Universitas Brawijaya)",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:roLk4NBRz8UC",
+      "index": 8,
+      "authors": "Luthfi Anshori, Rekyan Regasari Mardi Putri, Tibyani Tibyani",
+      "journal_year": "Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer 2 (7), 2745-2753, 2018",
+      "year": "2018",
+      "citations": 23,
+      "publication_date": "2018",
+      "journal": "Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer",
+      "jilid": "2",
+      "terbitan": "7",
+      "halaman": "2745-2753",
+      "abstract": "Prodi Informatika Universitas Brawijaya mempunyai 5 (lima) keminatan yang akan ditempuh mahasiswa dalam menjalani perkuliahan. Mengingat sangat pentingnya keminatan ini untuk perkuliahan mahasiswa yang nantinya juga berpengaruh pada tugas akhir (skripsi) hingga kelulusan mahasiswa, maka mahasiswa diharapkan untuk memilih keminatan yang sesuai dengan minat dan bakatnya. Berdasarkan Buku Pedoman Fakultas Ilmu Komputer Universitas Brawijaya tahun 2016 keminatan yang ada pada prodi Informatika ada 5 (lima) keminatan yaitu Rekayasa Perangkat Lunak, Komputasi Cerdas, Perangkat bergerak, Jaringan Komputer serta Game. Agar mahasiswa dapat memilih keminatan yang sesuai maka di perlukan sistem rekomendasi untuk keminatan mahasiswa dengan harapan mahasiswa dapat memilih keminatan yang sesuai dengan bakat, keinginan serta tentu saja nilai mata kuliah wajib …",
+      "artikel_scholar": "Implementasi Metode K-Nearest Neighbor untuk Rekomendasi Keminatan Studi (Studi Kasus: Jurusan Teknik Informatika Universitas Brawijaya)L Anshori, RRM Putri, T Tibyani - Jurnal Pengembangan Teknologi Informasi dan Ilmu …, 2018Dirujuk 23 kaliArtikel terkait3 versi"
+    },
+    {
+      "title": "Sistem Pendukung Keputusan Seleksi Beasiswa PPA dan BBM Menggunakan Metode Fuzzy AHP",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:ufrVoPGSRksC",
+      "index": 9,
+      "authors": "Fauziah Mayasari Iskandar, Arief Andy Soebroto, Rekyan Regasari",
+      "journal_year": "STIKI Inform. J 3 (1), 2-11, 2013",
+      "year": "2013",
+      "citations": 20,
+      "publication_date": "2013",
+      "journal": "STIKI Inform. J",
+      "jilid": "3",
+      "terbitan": "1",
+      "halaman": "2-11",
+      "abstract": "Each institute of education like university offered scholarships for students who have a good achievement and lack of financial. To assured that Academic Achievement Scholarship (PPA) and Student Learning Assistance (BBM) delivered to the right person, we need a comprehensive system to make decisions. The selection process of PPA and BBM scholarships is a problem which recently discussed by students because there is a probability that the distribution is not well targeted, the time is overdue, and the amount is inappropriate. We can use Fuzzy AHP method for this Decision Support System (DSS). AHP model can represent a problem into a hierarchy with levels: objectives, criteria, and alternatives and the fuzzy logic is used to minimize uncertainty value in AHP with crisp value. The analysis of software requirement system consists of actor’s identification and requirement list. Implementation of web-based system use HTML and PHP programming language that integrated with MySQL databases. The testing used are validation (Black Box) testing and accuracy testing. Black Box testing result is 100% which indicates that the functionality of the system fulfill the system requirements list. Accuracy testing result is 80% for PPA and 33.33% for BBM which indicate the DSS is running well with Fuzzy AHP method.",
+      "artikel_scholar": "Sistem Pendukung Keputusan Seleksi Beasiswa PPA dan BBM Menggunakan Metode Fuzzy AHPFM Iskandar, AA Soebroto, R Regasari - STIKI Inform. J, 2013Dirujuk 20 kaliArtikel terkait"
+    },
+    {
+      "title": "Pengembangan Sistem Pakar Diagnosa Penyakit Sapi Potong Dengan Metode Fuzzy K-Nearest Neighbour",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:zYLM7Y9cAGgC",
+      "index": 10,
+      "authors": "Restia Dwi Oktavianing Tyas, Arief Andy Soebroto, Muhammad Tanzil Furqon",
+      "journal_year": "Journal of Environmental Engineering and Sustainable Technology 2 (1), 58-66, 2015",
+      "year": "2015",
+      "citations": 15,
+      "publication_date": "2015/7/16",
+      "journal": "Journal of Environmental Engineering and Sustainable Technology",
+      "jilid": "2",
+      "terbitan": "1",
+      "halaman": "58-66",
+      "abstract": "Early detection and treatment of cow disease is an important thing for increasing productivity of beef. The dependence of the existence of an expert or veterinarian is too high. It is caused by a lack of knowledge of the breeder about cow disease. This is a condition in which an expert is needed. However, An expert or veterinarian is not always there every encountered, especially in country areas. Those problems can be solved by expert systems. This expert system using fuzzy K-Nearest Neighbour method to process the diagnosis. The results show the functional validation testing and system expertise by 100% and accuracy test variation k, variations training data and m by 97.56%.",
+      "artikel_scholar": "Pengembangan Sistem Pakar Diagnosa Penyakit Sapi Potong Dengan Metode Fuzzy K-Nearest NeighbourRDO Tyas, AA Soebroto, MT Furqon - Journal of Environmental Engineering and Sustainable …, 2015Dirujuk 15 kaliArtikel terkait6 versi",
+      "pdf_url": "https://jeest.ub.ac.id/index.php/jeest/article/download/31/61"
+    },
+    {
+      "title": "Pencarian Pasal Pada Kitab Undang-Undang Hukum Pidana (Kuhp) Berdasarkan Kasus Menggunakan Metode Cosine Similarity Dan Latent Semantic Indexing (Lsi)",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:UeHWp8X0CEIC",
+      "index": 11,
+      "authors": "Setyoko Yudho Baskoro, Achmad Ridok, Muhammad Tanzil Furqon",
+      "journal_year": "Journal of Environmental Engineering and Sustainable Technology 2 (2), 83-88, 2015",
+      "year": "2015",
+      "citations": 12,
+      "publication_date": "2015/12/6",
+      "journal": "Journal of Environmental Engineering and Sustainable Technology",
+      "jilid": "2",
+      "terbitan": "2",
+      "halaman": "83-88",
+      "abstract": "Indonesia is a country of law. As law states, Indonesian have regulations that govern the relationship between the communities, one of them is criminal law. Set of rules of criminal law is written in the Kitab Undang-undang Hukum Pidana (KUHP), which contains hundreds of clause which regulate the relationship between the community based on values, norms, and specific rules that focuses on the interests of the public. In this paper, information retrieval used to search the clause of the KUHP based on a description of the crime, using Latent Semantic Indexing (LSI). LSI adopts techniques in mathematical dimension reduction process Singular Value Decomposition (SVD). This system use 60 clause as training data, and 6 query or crime description as test data. In each of the data clause of the KUHP contained data such as clause number, clause, and the clause contents. The system will calculate and determine the relevant clause is based on query or description of the crimes that has been entered. Cosine similarity used to calculate the similarity or proximity clause KUHP with query. The performance of the system is shown by the test results of Mean Average Precision (MAP) value at each k-rank is 5, 10, 20, 30, 40, 50, and 59, with the highest performance is in k-rank 40 with MAP 0.8944.",
+      "artikel_scholar": "Pencarian Pasal Pada Kitab Undang-Undang Hukum Pidana (Kuhp) Berdasarkan Kasus Menggunakan Metode Cosine Similarity Dan Latent Semantic Indexing (Lsi)SY Baskoro, A Ridok, MT Furqon - Journal of Environmental Engineering and Sustainable …, 2015Dirujuk 12 kaliArtikel terkait8 versi",
+      "pdf_url": "https://jeest.ub.ac.id/index.php/jeest/article/download/36/65"
+    },
+    {
+      "title": "Prediksi Nilai Mata Kuliah Mahasiswa Menggunakan Algoritma K-Apriori",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:W7OEmFMy1HYC",
+      "index": 12,
+      "authors": "Lailil Muflikhah, W Lisa Yunita, M Tanzil Furqon",
+      "journal_year": "SISFO Vol 6 No 2 6, 2017",
+      "year": "2017",
+      "citations": 11,
+      "publication_date": "2017",
+      "journal": "SISFO Vol 6 No 2",
+      "jilid": "6",
+      "abstract": "Tujuan utama dari prediksi nilai mata kuliah adalah membantu mahasiswa mengambil mata kuliah pilihan secara tepat. Kebanyakan mahasiswa mengambil mata kuliah didasarkan pada jumlah mahasiswa mengambil matakuliah. Sekumpulan transkrip mahasiswa dapat dianalisis pola keterkaitan (association rule) antar nilai matakuliah. K-Apriori merupakan metode data mining untuk mencari pola keterkaitan nilai mata kuliah sehingga dapat digunakan memprediksi nilai mata kuliah lain. Tahapan utama metode ini meliputi mengelompokkan data menggunakan metode K-Means dan menemukan pola nilai mata kuliah menggunakan Apriori. Namun terdapat kekosongan nilai karena seluruh mata kuliah yang ditawarkan tidak diambil setiap mahasiswa. Oleh karenanya, dilakukan preprocessing data menggunakan Wiener Transformation sebelum dicari polanya. Pengujian didasarkan tingkat kemampuan akademik mahsiswa dengan minimum support dan confidence sebesar 10% dan lift ratio> 1. Hasilnya, rule yang dibangkitkan dari IPK di bawah dan di atas rata-rata memiliki tingkat kesalahan sebesar 8.75% dan 8.5%. Sedangkan jika rule dibangkitkan dari IPK rata-rata memiliki kesalahan sebesar 11%.",
+      "artikel_scholar": "Prediksi Nilai Mata Kuliah Mahasiswa Menggunakan Algoritma K-AprioriL Muflikhah, WL Yunita, MT Furqon - SISFO Vol 6 No 2, 2017Dirujuk 11 kaliArtikel terkait6 versi",
+      "pdf_url": "http://is.its.ac.id/pubs/oajis/index.php/file/download_file/1696"
+    },
+    {
+      "title": "Sistem Pendukung Keputusan Pemilihan Atlet Yang Layak Masuk Tim Pencak Silat Dengan Metode Simple Additive Weighting (SAW)",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:kNdYIx-mwKoC",
+      "index": 13,
+      "authors": "Rizky Bangkit, Rekyan Regasari, WF Mahmudy",
+      "journal_year": "Jurnal Mahasiswa PTNK 4 (4), 2014",
+      "year": "2014",
+      "citations": 9,
+      "publication_date": "2014",
+      "journal": "Jurnal Mahasiswa PTNK",
+      "jilid": "4",
+      "terbitan": "4",
+      "artikel_scholar": "Sistem Pendukung Keputusan Pemilihan Atlet Yang Layak Masuk Tim Pencak Silat Dengan Metode Simple Additive Weighting (SAW)R Bangkit, R Regasari, WF Mahmudy - Jurnal Mahasiswa PTNK, 2014Dirujuk 9 kaliArtikel terkait"
+    },
+    {
+      "title": "Implementasi Fuzzy Time Series Pada Prediksi Harga Daging Di Pasar Kabupaten Malang",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:8k81kl-MbHgC",
+      "index": 14,
+      "authors": "Frans Agum Gumelar, Rekyan Regasari Mardi Putri, Indriati Indriati",
+      "journal_year": "Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer 2 (8), 2724-2733, 2018",
+      "year": "2018",
+      "citations": 7,
+      "publication_date": "2018",
+      "journal": "Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer",
+      "jilid": "2",
+      "terbitan": "8",
+      "halaman": "2724-2733",
+      "abstract": "Daging sapi merupakan salah satu bahan pokok yang sering dikonsumsi oleh masyarakat Indonesia. Kelangkaan daging sapi lokal merupakan salah satu penyebab terjadinya kenaikan harga. Menstabilkan harga daging sapi adalah tugas dari DISPERINDAG. DISPERINDAG tersebar hampir di setiap daerah di Indonesia, salah satunya adalah di Kabupaten Malang. Kabupaten Malang yang kian taun jumlah penduduknya kian bertambah mengakibatkan kebutuhan pangan khususnya konsumsi daging sapi mengalami peningkatan. Meningkatnya permintaan harga daging sapi juga mempengaruhi kenaikan harga daging sapi tersebut. Sehingga DISPERINDAG harus mengontrol kenaikan harga daging sapi supaya tidak mengalami peningkatan yang drastis. Oleh karena itu salah satu upaya yang dapat dilakukan adalah melakukan peramalan kenaikan harga daging sapi. Sehingga pihak DISPERINDAG dapat mempertimbangkan jumlah harga daging sapi di bulan mendatang berdasarkan hasil ramalan. Proses peramalkan didasarkan pada data data historis yang sudah ada. Peramalan ini disebut peramalan data time series. Peramalan data time series lebih menekankan pada relasi antar data-data. Metode yang digunakan untuk peramalan adalah Fuzzy Time Series (FTS). Berdasarkan hasil pengujian menggunakan 21 data harga daging di Kabupaten Malang pada tahun 2016 dan 2017, akurasi yang didapat dari peramalan sebesar 57%. Dengan nilai eror terkecil terletak di bulan juni 2017 sebesar 16,129 dan nilai eror terbesar terletak di bulan maret 2016 sebesar 65,610,000.",
+      "artikel_scholar": "Implementasi Fuzzy Time Series Pada Prediksi Harga Daging Di Pasar Kabupaten MalangFA Gumelar, RRM Putri, I Indriati - Jurnal Pengembangan Teknologi Informasi dan Ilmu …, 2018Dirujuk 7 kaliArtikel terkait4 versi",
+      "pdf_url": "https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/download/1942/747"
+    },
+    {
+      "title": "Sistem Pakar Diagnosa Penyakit Kulit Pada Anak Menggunakan Metode Certainty Factor",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:u5HHmVD_uO8C",
+      "index": 15,
+      "authors": "Dzurratul Ulya, R Regasari, Muhammad Tanzil Furqon",
+      "journal_year": "Sistem Pakar Diagnosa Penyakit Kulit Pada Anak Menggunakan Metode Certainty …, 2014",
+      "year": "2014",
+      "citations": 5,
+      "publication_date": "2014/7/14",
+      "journal": "Sistem Pakar Diagnosa Penyakit Kulit Pada Anak Menggunakan Metode Certainty Factor",
+      "halaman": "1-11",
+      "abstract": "Di negara berkembang, saat ini masih belum cukup tenaga ahli kesehatan khususnya spesialis kulit untuk anak. Kulit yang merupakan organ terbesar dari tubuh manusia yang menjadi pertahanan pertama tubuh dari serangan bakteri dan virus. Ketika kulit terkena matahari, cuaca kering, atau bakteri, maka reaksinya akan merembet ke bagian tubuh lain, bahkan dapat berakibat pada kematian jika terlambat ditangani terutama jika penderitanya anak-anak yang sangat rentan akan serangan penyakit. Pada penelitian ini masalah-masalah tersebut diselesaikan dengan membuat sistem pakar (Expert System) yang dapat mempercepat dalam mendiagnosa suatu jenis penyakit kulit pada anak, sehingga dapat dengan mudah diketahui jenis penyakit yang sedang menjangkit. Dimana sistem ini menggunakan metode kepastian (Certainty Factor). Sehingga diharapkan sistem ini dapat menjadi suatu alternatif solusi untuk mengatasi masalah yang sering dialami oleh petugas kesehatan.",
+      "artikel_scholar": "Sistem Pakar Diagnosa Penyakit Kulit Pada Anak Menggunakan Metode Certainty FactorD Ulya, R Regasari, MT Furqon - Sistem Pakar Diagnosa Penyakit Kulit Pada Anak …, 2014Dirujuk 5 kaliArtikel terkait2 versi",
+      "pdf_url": "https://www.academia.edu/download/40792189/DR00043201412_FIN.pdf"
+    },
+    {
+      "title": "Sutrisno.(2017). Implementasi Metode Profile Matching untuk Seleksi Penerimaan Anggota Asisten Praktikum (Studi Kasus: Laboratorium Pembelajaran Kelompok Praktikum Basis Data …",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:MXK_kJrjxJIC",
+      "index": 16,
+      "authors": "D Saputra, R Regasari, M Putri",
+      "journal_year": "Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer 1 (12), 1804-1812, 0",
+      "citations": 5,
+      "year": "",
+      "journal": "Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer",
+      "jilid": "1",
+      "terbitan": "12",
+      "halaman": "1804-1812",
+      "artikel_scholar": "Sutrisno.(2017). Implementasi Metode Profile Matching untuk Seleksi Penerimaan Anggota Asisten Praktikum (Studi Kasus: Laboratorium Pembelajaran Kelompok Praktikum Basis Data FILKOM)D Saputra, R Regasari, M Putri - Jurnal Pengembangan Teknologi Informasi Dan Ilmu …Dirujuk 5 kaliArtikel terkait"
+    },
+    {
+      "title": "Paralelisasi Algoritma K-Medoid Pada Gpu Menggunakan Open Cl",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:IjCSPb-OGe4C",
+      "index": 17,
+      "authors": "Muhammad Tanzil Furqon, Achmad Ridok, Wayan Firdaus Mahmudy",
+      "journal_year": "Konf. Nas. Sist. Inf, 408-413, 2015",
+      "year": "2015",
+      "citations": 3,
+      "publication_date": "2015",
+      "journal": "Konf. Nas. Sist. Inf",
+      "halaman": "408-413",
+      "abstract": "Abstrak k-Medoid banyak digunakan karena kemampuannya dalam mendeteksi outlier dalam proses clustering-nya. Namun demikian, kelemahan algoritma ini adalah kompleksitas komputasinya yang tinggi sehingga berdampak pada performa clustering secara keseluruhan. Selain itu, proses pemilihan k jumlah pusat cluster awal secara random membuat hasil proses clustering menjadi tidak stabil. Oleh sebab itu, dalam penelitian ini dirancang algoritma paralel k-Medoid untuk meningkatkan performa serta melakukan optimasi terhadap kelemahan yang ada pada algoritma k-Medoid tradisional. Optimasi yang dilakukan menggunakan metode Cluster Validity Index untuk menentukan jumlah pusat cluster awal dan paralelisasi algoritma k-Medoid menggunakan Open CL. Hasil eksperimen menunjukkan bahwa algoritma paralel k-Medoid mempunyai kualitas hasil clustering yang stabil dan memiliki tingkat performa tinggi, yaitu mencapai 364 kali lebih cepat dibandingkan algoritma k-Medoid tradisional.",
+      "artikel_scholar": "Paralelisasi Algoritma K-Medoid Pada Gpu Menggunakan Open ClMT Furqon, A Ridok, WF Mahmudy - Konf. Nas. Sist. Inf, 2015Dirujuk 3 kaliArtikel terkait3 versi",
+      "pdf_url": "http://wayanfm.lecture.ub.ac.id/files/2015/03/2015-Paralelisasi-algoritma-k-Medoid-pada-GPU-menggunakan-Open-CL-OK.pdf"
+    },
+    {
+      "title": "Sistem Identifikasi Genre Musik dengan Metode Ekstraksi Fitur FFT dan Metode Klasifikasi Linear Discriminant Analysis Beserta Similarity Measure",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:Tyk-4Ss8FVUC",
+      "index": 18,
+      "authors": "IDG Anthasenna, Wayan Firdaus Mahmudy, M Tanzil Furqon",
+      "journal_year": "Universitas Brawijaya, 2014",
+      "year": "2014",
+      "citations": 3,
+      "publication_date": "2014/7/14",
+      "journal": "Universitas Brawijaya",
+      "abstract": "Musik merupakan salah satu seni atau hiburan beserta aktivitas manusia. Musik terdiri dari suara–suara yang teratur. Beberapa musik dikelompokan ke dalam suatu genre yang dinamakan genre musik. Setiap hari, seseorang setidaknya mendengarkan beberapa buah musik dan setiap orang mempunyai selera yang berbeda dalam memlihi genre musk. Dalam penelitian ini dilakukan kombinasi kedua metode yaitu Linear Discriminant Value dan Cosine Similarity yang digunakan untuk memprediksi genre suatu musik. Kedua metode tersebut akan memproses fitur–fitur yang dihasilkan dari musik tersebut. Fitur dalam penelitian ini dibedakan menjadi dua jenis yaitu fitur onevalue dan multivalue. Linear",
+      "artikel_scholar": "Sistem Identifikasi Genre Musik dengan Metode Ekstraksi Fitur FFT dan Metode Klasifikasi Linear Discriminant Analysis Beserta Similarity MeasureIDG Anthasenna, WF Mahmudy, MT Furqon - Universitas Brawijaya, 2014Dirujuk 3 kaliArtikel terkait2 versi",
+      "pdf_url": "https://www.researchgate.net/profile/Wayan-Mahmudy-2/publication/311804428_SISTEM_IDENTIFIKASI_GENRE_MUSIK_DENGAN_METODE_EKSTRAKSI_FITUR_FFT_DAN_METODE_KLASIFIKASI_LINEAR_DISCRIMINANT_ANALYSIS_BESERTA_SIMILARITY_MEASURE/links/585b189808ae329d61f14a35/SISTEM-IDENTIFIKASI-GENRE-MUSIK-DENGAN-METODE-EKSTRAKSI-FITUR-FFT-DAN-METODE-KLASIFIKASI-LINEAR-DISCRIMINANT-ANALYSIS-BESERTA-SIMILARITY-MEASURE.pdf"
+    },
+    {
+      "title": "Sistem Pendukung Keputusan untuk Proses Penentuan Rumah Tangga Miskin Menggunakan Metode Weighted Product.",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:Y0pCki6q_DkC",
+      "index": 19,
+      "authors": "Novita Rudiarsih",
+      "journal_year": "Universitas Brawijaya, 2012",
+      "year": "2012",
+      "citations": 3,
+      "publication_date": "2012/10/19",
+      "lembaga": "Universitas Brawijaya",
+      "abstract": "Penghapusan subsidi Bahan Bakar Minyak (BBM) oleh pemerintah berdampak pada kenaikan biaya hidup semua lapisan masyarakat. Namun, pemerintah akan memberikan kompensasi atas kenaikan harga BBM tersebut kepada rumah tangga miskin berupa Bantuan Langsung Tunai (BLT) agar tidak memberatkan. Penentuan rumah tangga miskin selama ini masih bersifat subyektif berdasarkan pendapat tokoh masyarakat sehingga bantuan tidak tepat sasaran. Oleh karena itu, perlu dibangun Sistem Pendukung Keputusan (SPK) untuk membantu pemerintah dalam proses penentuan rumah tangga miskin menggunakan metode Weighted Product (WP). Metode ini digunakan untuk menghitung skor setiap rumah tangga berdasarkan kriteria kemiskinan yang ditentukan Badan Pusat Statistik (BPS). Sistem ini dibangun menggunakan bahasa pemrograman berorientasi objek yaitu Java dan MySQL untuk pengolahan data. Pengujian sistem dilakukan dengan cara membandingkan hasil perhitungan sistem dengan hasil perhitungan menggunakan rumus BPS. Hasil pengujian menunjukkan tingkat akurasi sistem mencapai 93.33%.",
+      "artikel_scholar": "Sistem Pendukung Keputusan untuk Proses Penentuan Rumah Tangga Miskin Menggunakan Metode Weighted Product.N Rudiarsih - 2012Dirujuk 3 kaliArtikel terkait"
+    },
+    {
+      "title": "Implementasi Metode Artificial Bee Colony-Kmeans (ABCKM) Untuk Pengelompokan Biji Wijen Berdasarkan Sifat Warna Cangkang Biji",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&citation_for_view=vcAqZLAAAAAJ:3fE2CSJIrl8C",
+      "index": 20,
+      "authors": "Enny Trisnawati, Rekyan Regasari, Sutrisno Sutrisno",
+      "journal_year": "Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer 2 (3), 1337-1347, 2018",
+      "year": "2018",
+      "citations": 2,
+      "publication_date": "2018",
+      "journal": "Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer",
+      "jilid": "2",
+      "terbitan": "3",
+      "halaman": "1337-1347",
+      "abstract": "Wijen merupakan salah satu penghasil minyak nabati yang tingkat konsumsi di dunia diperkirakan akan terus meningkat seiring dengan banyaknya manfaat dan kegunaannya. Harga jual wijen ditentukan oleh kualitas wijen. Indikator yang dapat digunakan sebagai petunjuk kualitas wijen adalah warna pada cangkang biji. Usaha untuk menghasilkan wijen kualitas terbaik salah satunya dengan cara persilangan antar kultivar yang menghasilkan beragam warna biji wijen sehingga perlu dikelompokan berdasarkan kedekatan warnanya. Beberapa cara yang sudah dilakukan peneliti terdahulu untuk mengelompokan biji wijen seperti metode kualitatif dan kuantitatif. Saat ini, ada 3 model metode kuantitatif untuk pengelompokan biji wijen yaitu metode IWOKM, PSO-K-Means dan GA-KMeans yang hasil pengelompokan datanya cukup baik. Pada penelitian ini digunakan metode ABCKM yang merupakan gabungan dari metode KMeans (KM) dan Artificial Bee Colony (ABC). Performa dari ABCKM selanjutnya akan dibandingkan dengan metode KM, IWOKM, PSO-K-Means dan GA-KMeans. Berdasarkan hasil pengujian perbandingan metode, metode ABCKM terbukti lebih baik daripada metode KM dan metode sebelumnya: IWOKM, GA-KMEANS dan PSO-K-Means dalam mengelompokan data wijen. Hal ini terbukti dengan nilai rata-rata fitness dan nilai rata-rata silhoutte coeficient dari ABCKM lebih baik dari KM, IWOKM, GA-KMEANS dan PSO-K-Means. Hasil pengelompokan metode ABCKM sama dengan metode sebelumnya yaitu C1: C2= 233: 58, sehingga dapat menjadi metode alternatif untuk mengelompokan biji wijen berdasarkan sifat …",
+      "artikel_scholar": "Implementasi Metode Artificial Bee Colony-Kmeans (ABCKM) Untuk Pengelompokan Biji Wijen Berdasarkan Sifat Warna Cangkang BijiE Trisnawati, R Regasari, S Sutrisno - Jurnal Pengembangan Teknologi Informasi dan Ilmu …, 2018Dirujuk 2 kaliArtikel terkait5 versi",
+      "pdf_url": "http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/download/1158/432"
+    },
+    {
+      "title": "PERBEDAAN PENGARUH METODE PENGAJARAN DIRECT DAN INDIRECT TERHADAP HASIL BELAJAR LOMPAT JAUH GAYA JONGKOK DITINJAU DARI BODY MASS INDEX",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&cstart=20&pagesize=80&citation_for_view=vcAqZLAAAAAJ:YsMSGLbcyi4C",
+      "index": 21,
+      "authors": "Nur Sulistyo Mutaqin, Muhammad Furqon, Agus Kristyanto",
+      "journal_year": "Jurnal Pendidikan Jasmani Kesehatan dan Rekreasi (Penjaskesrek) 2 (1), 1-17, 2015",
+      "year": "2015",
+      "citations": 0,
+      "publication_date": "2015/1/19",
+      "journal": "Jurnal Pendidikan Jasmani Kesehatan dan Rekreasi (Penjaskesrek)",
+      "jilid": "2",
+      "terbitan": "1",
+      "halaman": "1-17",
+      "abstract": "Penelitian ini bertujuan untuk mengetahui:(1) Perbedaan pengaruh antara metode pengajaran direct dan indirect terhadap hasil belajar lompat jauh gaya jongkok,(2) Perbedaan hasil belajar lompat jauh gaya jongkok antara mahasiswa yang memiliki Body Mass Index (BMI)> 25, Body Mass Index (BMI)= 18, 5-25, dan Body Mass Index (BMI)< 17,(3) Pengaruh interaksi antara metode pengajaran dan body mass index terhadap hasil belajar lompat jauh gaya jongkok.",
+      "artikel_scholar": "PERBEDAAN PENGARUH METODE PENGAJARAN DIRECT DAN INDIRECT TERHADAP HASIL BELAJAR LOMPAT JAUH GAYA JONGKOK DITINJAU DARI BODY MASS INDEXNS Mutaqin, M Furqon, A Kristyanto - Jurnal Pendidikan Jasmani Kesehatan dan Rekreasi …, 2015Artikel terkait3 versi"
+    },
+    {
+      "title": "AKREDITASI PROGRAM STUDI SARJANA MENGGUNAKAN METODE ANALYTIC HIERARCHY PROCESS (AHP)",
+      "url": "https://scholar.google.com/citations?view_op=view_citation&hl=id&user=vcAqZLAAAAAJ&cstart=20&pagesize=80&citation_for_view=vcAqZLAAAAAJ:LkGwnXOMwfcC",
+      "index": 22,
+      "authors": "Niken Hendrakusma Wardani, Arief Andy Soebroto, Rekyan Regasari",
+      "journal_year": "",
+      "citations": 0,
+      "year": "",
+      "abstract": "Setiap program studi sarjana dari perguruan tinggi negeri maupun swasta yang ada di Indonesia memerlukan penilaian akreditasi sebagai kendali mutu dan akuntabilitas publik institusi. Pencapaian predikat terakreditasi A dari Badan Akreditasi Nasional Perguruan Tinggi (BAN-PT) bukanlah hal yang mudah dilakukan dalam waktu singkat. Keterbatasan sumber daya manusia, dana, waktu dan penilaian BAN-PT dijadikan sebagai pertimbangan ketua program studi (kaprodi) untuk perbaikan akreditasi. Sistem Pendukung Keputusan (SPK) dibuat untuk membantu kaprodi dalam menyusun prioritas perbaikan tujuh standar akreditasi berdasarkan pertimbangan kondisi program studi. Metode Analytic Hierarchy Process (AHP) merupakan salah satu metode dalam Multiple Criteria Decision Making (MCDM) yang mampu menguraikan sebuah masalah ke bentuk hierarki dengan level: tujuan, kriteria, dan alternatif [1]. Perangkat lunak yang dikembangkan menggunakan bahasa pemrograman PHP dan HTML. Hasil pengujian fungsionalitas terhadap 12 test case dengan metode black-box testing menunjukkan bahwa sistem ini 100% valid untuk memenuhi daftar kebutuhan sistem. Pengujian proses perankingan dan User Acceptance Test (UAT) dilakukan terhadap 7 objek uji. Hasilnya menunjukkan bahwa sistem dapat diterima dan bekerja dengan baik untuk menentukan prioritas perbaikan standar akreditasi secara ideal (menggunakan perhitungan matematis metode AHP) berdasarkan bobot kriteria dan kondisi program studi.",
+      "artikel_scholar": "AKREDITASI PROGRAM STUDI SARJANA MENGGUNAKAN METODE ANALYTIC HIERARCHY PROCESS (AHP)NH Wardani, AA Soebroto, R RegasariArtikel terkait"
+    }
+  ],
+  "abstracts_collected": 20,
+  "profile_url": "https://scholar.google.com/citations?hl=id&user=vcAqZLAAAAAJ"
+}

faculty_summary.csv

@@ -0,0 +1,29 @@
+Name,Affiliation,Email_Verification,Research_Interests,Total_Publications,Abstracts_Collected,Profile_URL,Safe_Filename,Last_Updated
+candra dewi,"Dosen Teknik Informatika, Fakultas Ilmu Komputer,Universitas Brawijaya",Verified email at ub.ac.id -Homepage,Kecerdasan buatan,265,265,https://scholar.google.com/citations?user=HhuEl-EAAAAJ&hl=en,,
+Budi Darma Setiawan,"Informatics,Universitas Brawijaya",Email yang diverifikasi di ub.ac.id,Pattern Recognition; Behavior Informatics; Smart City,45,38,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,budi_darma_setiawan,2025-06-15 07:03:01
+Achmad Ridok,Filkom Universitas Brawijaya,Email yang diverifikasi di ub.ac.id,Information Retrieval; Bioinformatics; Text Mining,71,69,https://scholar.google.com/citations?hl=id&user=Xsq9QEwAAAAJ,achmad_ridok,2025-06-15 07:05:22
+Agus Wahyu Widodo,Fakultas Ilmu KomputerUniversitas Brawijaya,Email yang diverifikasi di ub.ac.id -Beranda,Image Processing and Meta Heuristic Optimization,92,63,https://scholar.google.com/citations?hl=id&user=HGoG7nQAAAAJ,agus_wahyu_widodo,2025-06-15 07:14:03
+candra dewi,"Dosen Teknik Informatika, Fakultas Ilmu Komputer,Universitas Brawijaya",Email yang diverifikasi di ub.ac.id -Beranda,Kecerdasan buatan,265,265,https://scholar.google.com/citations?user=HhuEl-EAAAAJ&hl=id,candra_dewi,2025-06-15 07:27:22
+Arief Andy Soebroto,Universitas Brawijaya,Email yang diverifikasi di ub.ac.id -Beranda,Kecerdasan Buatan,156,151,https://scholar.google.com/citations?hl=id&user=ALQDmj0AAAAJ,arief_andy_soebroto,2025-06-15 07:28:32
+Bayu Rahayudi,Brawijaya University,Email yang diverifikasi di ub.ac.id,Artificial Intelligent; Visualization,233,227,https://scholar.google.com/citations?hl=id&user=Lz5dgAgAAAAJ,bayu_rahayudi,2025-06-15 07:50:05
+Dian Eka Ratnawati,"Filkom,Universitas Brawijaya",Email yang diverifikasi di ub.ac.id,Komputasi Cerdas,275,264,https://scholar.google.com/citations?hl=id&user=A-XSItgAAAAJ,dian_eka_ratnawati,2025-06-15 07:52:41
+Diva Kurnianingtyas,Department of Informatics Engineering,Email yang diverifikasi di ub.ac.id -Beranda,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,49,46,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,diva_kurnianingtyas,2025-06-15 07:57:30
+Irawati Nurmala Sari,Brawijaya University,Tidak ada email yang diverifikasi,Image Processing; Image Inpainting; Deep Learning,16,14,https://scholar.google.com/citations?hl=id&user=d4vypz8AAAAJ,irawati_nurmala_sari,2025-06-15 07:59:21
+Lailil Muflikhah,"Faculty of Computer Science, University of Brawjaya",Email yang diverifikasi di ub.ac.id,Data Mining; Artificial Intelligent,169,166,https://scholar.google.com/citations?hl=id&user=JH-vXuQAAAAJ,lailil_muflikhah,2025-06-15 08:15:05
+Edy Santoso,"Faculty of Computer Science,Brawijaya University",Email yang diverifikasi di ub.ac.id -Beranda,Articial Intellegence; Intelligent Computing,289,280,https://scholar.google.com/citations?hl=id&user=xk90nCQAAAAJ,edy_santoso,2025-06-15 08:16:44
+Muh Arif Rahman,Lab komputasi Cerdas Fakultas Ilmu Komputer,Email yang diverifikasi di ub.ac.id,Image Processing; computer vision,178,166,https://scholar.google.com/citations?hl=id&user=PYPVMEUAAAAJ,muh_arif_rahman,2025-06-15 08:31:39
+Fitra Abdurrachman Bachtiar,Brawijaya University,Email yang diverifikasi di ub.ac.id -Beranda,Affective Computing; Affective Engineering; Intelligent System; HCI; Data Mining,180,178,https://scholar.google.com/citations?hl=id&user=txHE0bQAAAAJ,fitra_abdurrachman_bachtiar,2025-06-15 08:33:34
+Muhammad Tanzil Furqon,Universitas Brawijaya,Email yang diverifikasi di ub.ac.id,Data mining; artificial intelligence; machine learning,191,184,https://scholar.google.com/citations?hl=id&user=Zj9vsUAAAAAJ,muhammad_tanzil_furqon,2025-06-15 08:49:22
+Imam Cholissodin,Universitas Brawijaya,Email yang diverifikasi di ub.ac.id -Beranda,Artificial Intelligence; Pattern Recognition; Information Retrieval; Decision Support System; Mobile Programming,322,318,https://scholar.google.com/citations?hl=id&user=2WTulU4AAAAJ,imam_cholissodin,2025-06-15 09:03:16
+Novanto Yudistira,"Hiroshima University, Faculty of Computer Science,Universitas Brawijaya",Email yang diverifikasi di ub.ac.id -Beranda,Deep Learning; Computer Vision; Pattern Recognition; Artificial Intelligence,195,189,https://scholar.google.com/citations?hl=id&user=NxSJRlYAAAAJ,novanto_yudistira,2025-06-15 09:08:29
+Hidayat Nurul,Universitas Brawijaya,Email yang diverifikasi di ub.ac.id,Expert System,158,10,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,hidayat_nurul,2025-06-15 09:24:30
+Indriati,Brawijaya University,Email yang diverifikasi di ub.ac.id -Beranda,Information Retrieval; Intelligent system,225,217,https://scholar.google.com/citations?hl=id&user=ZHrqtYcAAAAJ,indriati,2025-06-15 09:27:13
+Rekyan Regasari M. P.,"Lecturer,Universitas Brawijaya",Email yang diverifikasi di ub.ac.id -Beranda,Artificial Intelligence; Machine Learning; Data Mining; big data; education,22,20,https://scholar.google.com/citations?hl=id&user=vcAqZLAAAAAJ,rekyan_regasari_m_p,2025-06-15 09:29:36
+Rizal Setya Perdana,"Faculty of Computer Science,Brawijaya University",Email yang diverifikasi di ub.ac.id -Beranda,machine learning; deep neural network,108,108,https://scholar.google.com/citations?hl=id&user=2o91uV4AAAAJ,rizal_setya_perdana,2025-06-15 09:39:49
+Putra Pandu Adikara,Brawijaya University,Email yang diverifikasi di ub.ac.id -Beranda,Information Retrieval; Natural Language Processing; Social Media Analysis; Text Mining; Image Processing,187,176,https://scholar.google.com/citations?hl=id&user=Euc39FcAAAAJ,putra_pandu_adikara,2025-06-15 09:41:49
+Sigit Adinugroho,"Faculty of Computer Science,Brawijaya University",Email yang diverifikasi di ub.ac.id,Image Processing,126,88,https://scholar.google.com/citations?hl=id&user=01qz25oAAAAJ,sigit_adinugroho,2025-06-15 10:18:52
+Randy Cahya Wihandika,"Faculty of Computer Science,Brawijaya University",Email yang diverifikasi di ub.ac.id,Image Processing; Computer Vision,150,147,https://scholar.google.com/citations?hl=id&user=qpvbsqUAAAAJ,randy_cahya_wihandika,2025-06-15 10:37:18
+Tirana Noor Fatyanosa,"Assistant Professor,Brawijaya University",Email yang diverifikasi di ub.ac.id -Beranda,Language Modeling; Natural Language Processing; Deep Learning; Meta-learning; Evolutionary Algorithm,42,42,https://scholar.google.com/citations?hl=id&user=odb3L2UAAAAJ,tirana_noor_fatyanosa,2025-06-15 10:41:28
+Wayan Firdaus Mahmudy,"Professor,University of Brawijaya",Email yang diverifikasi di ub.ac.id -Beranda,Artificial Intelligence; Genetic Algorithms; Manufacturing Engineering,395,360,https://scholar.google.com/citations?hl=id&user=E40omM8AAAAJ,wayan_firdaus_mahmudy,2025-06-15 11:23:47
+Yuita Arum Sari,"Assistant professor, Faculty of Computer Science ,Brawijaya University",Email yang diverifikasi di ub.ac.id -Beranda,Digital Signal Processing; AI; Health Informatics; Computer Vision; Machine Learning,202,198,https://scholar.google.com/citations?hl=id&user=J5gQoNcAAAAJ,yuita_arum_sari,2025-06-15 11:45:14
+Suprapto,"Faculty of Computer Science,Brawijaya University",Email yang diverifikasi di ub.ac.id -Beranda,image processing; data mining; decision support system; IT Governance,1043,1006,https://scholar.google.com/citations?hl=id&user=iPmgukUAAAAJ,suprapto,2025-06-15 17:23:23

lecturer_topics.json

@@ -0,0 +1,2338 @@
+{
+  "Arief Andy Soebroto": {
+    "general_topics": [
+      {
+        "topic": "Machine Learning",
+        "count": 22
+      },
+      {
+        "topic": "Web Development",
+        "count": 13
+      },
+      {
+        "topic": "Information Systems",
+        "count": 12
+      },
+      {
+        "topic": "Decision Support Systems",
+        "count": 9
+      },
+      {
+        "topic": "Artificial Intelligence",
+        "count": 8
+      },
+      {
+        "topic": "Deep Learning",
+        "count": 8
+      },
+      {
+        "topic": "Software Development",
+        "count": 8
+      },
+      {
+        "topic": "Expert Systems",
+        "count": 6
+      },
+      {
+        "topic": "Fuzzy Logic",
+        "count": 5
+      },
+      {
+        "topic": "Agriculture",
+        "count": 5
+      }
+    ],
+    "specific_topics": [
+      {
+        "topic": "Machine Learning for Rainfall Forecasting",
+        "count": 3
+      },
+      {
+        "topic": "Decision Support Systems for Early Stroke Detection",
+        "count": 3
+      },
+      {
+        "topic": "Artificial Intelligence for Teaching Materials",
+        "count": 2
+      },
+      {
+        "topic": "Machine Learning for Covid-19 Case Prediction",
+        "count": 2
+      },
+      {
+        "topic": "Optimization Algorithms for Extreme Learning Machine",
+        "count": 2
+      },
+      {
+        "topic": "Fuzzy Logic for Medical Diagnosis",
+        "count": 2
+      },
+      {
+        "topic": "Artificial Intelligence for Theory and Implementation",
+        "count": 1
+      },
+      {
+        "topic": "Machine Learning for Theory and Implementation",
+        "count": 1
+      },
+      {
+        "topic": "Deep Learning for Theory and Implementation",
+        "count": 1
+      },
+      {
+        "topic": "Artificial Intelligence for Overview",
+        "count": 1
+      }
+    ],
+    "total_general_unique": 88,
+    "total_specific_unique": 199
+  },
+  "Rekyan Regasari M. P.": {
+    "general_topics": [
+      {
+        "topic": "Biometrics",
+        "count": 1
+      },
+      {
+        "topic": "Human Resources",
+        "count": 1
+      },
+      {
+        "topic": "Deep Learning",
+        "count": 1
+      },
+      {
+        "topic": "Chemistry",
+        "count": 1
+      }
+    ],
+    "specific_topics": [
+      {
+        "topic": "Biometrics for Identity and Data Security",
+        "count": 1
+      },
+      {
+        "topic": "Human Resources for Data Security",
+        "count": 1
+      },
+      {
+        "topic": "Deep Learning for Molecular Structure Classification",
+        "count": 1
+      },
+      {
+        "topic": "Chemistry for Active Compound Function Classification",
+        "count": 1
+      }
+    ],
+    "total_general_unique": 4,
+    "total_specific_unique": 4
+  },
+  "Diva Kurnianingtyas": {
+    "general_topics": [
+      {
+        "topic": "Healthcare",
+        "count": 8
+      },
+      {
+        "topic": "Machine Learning",
+        "count": 7
+      },
+      {
+        "topic": "Deep Learning",
+        "count": 6
+      },
+      {
+        "topic": "Optimization",
+        "count": 6
+      },
+      {
+        "topic": "System Dynamics",
+        "count": 6
+      },
+      {
+        "topic": "Computer Vision",
+        "count": 5
+      },
+      {
+        "topic": "Artificial Intelligence",
+        "count": 4
+      },
+      {
+        "topic": "Natural Language Processing",
+        "count": 3
+      },
+      {
+        "topic": "Swarm Intelligence",
+        "count": 3
+      },
+      {
+        "topic": "Genetic Algorithms",
+        "count": 2
+      }
+    ],
+    "specific_topics": [
+      {
+        "topic": "System Dynamics for Financial Strategy",
+        "count": 4
+      },
+      {
+        "topic": "Healthcare for National Health Insurance",
+        "count": 3
+      },
+      {
+        "topic": "Natural Language Processing for Sentiment Analysis",
+        "count": 2
+      },
+      {
+        "topic": "Healthcare for Social Health Insurance",
+        "count": 2
+      },
+      {
+        "topic": "Optimization for Dietary Recommendations",
+        "count": 2
+      },
+      {
+        "topic": "Nutrition for Maternal Health",
+        "count": 2
+      },
+      {
+        "topic": "Blockchain for Food Safety",
+        "count": 2
+      },
+      {
+        "topic": "Risk Management for Halal Certification",
+        "count": 2
+      },
+      {
+        "topic": "Deep Learning for LSTM and Word2Vec",
+        "count": 1
+      },
+      {
+        "topic": "Machine Learning for VADER and Transformers",
+        "count": 1
+      }
+    ],
+    "total_general_unique": 27,
+    "total_specific_unique": 60
+  },
+  "Imam Cholissodin": {
+    "general_topics": [
+      {
+        "topic": "Machine Learning",
+        "count": 61
+      },
+      {
+        "topic": "Optimization",
+        "count": 42
+      },
+      {
+        "topic": "Deep Learning",
+        "count": 23
+      },
+      {
+        "topic": "Natural Language Processing",
+        "count": 16
+      },
+      {
+        "topic": "Genetic Algorithms",
+        "count": 15
+      },
+      {
+        "topic": "Artificial Intelligence",
+        "count": 8
+      },
+      {
+        "topic": "Finance",
+        "count": 7
+      },
+      {
+        "topic": "Algorithm",
+        "count": 6
+      },
+      {
+        "topic": "Bioinformatics",
+        "count": 5
+      },
+      {
+        "topic": "Extreme Learning Machine",
+        "count": 5
+      }
+    ],
+    "specific_topics": [
+      {
+        "topic": "Natural Language Processing for Sentiment Analysis",
+        "count": 6
+      },
+      {
+        "topic": "Machine Learning for Disease Classification",
+        "count": 3
+      },
+      {
+        "topic": "Machine Learning for Sentiment Analysis on Twitter",
+        "count": 2
+      },
+      {
+        "topic": "Deep Learning for Cryptocurrency Price Prediction",
+        "count": 2
+      },
+      {
+        "topic": "Time Series Analysis for COVID-19 Case Prediction",
+        "count": 2
+      },
+      {
+        "topic": "Optimization for School Transport Routing",
+        "count": 2
+      },
+      {
+        "topic": "Optimization for School Transportation",
+        "count": 2
+      },
+      {
+        "topic": "Machine Learning for Cancer Classification",
+        "count": 2
+      },
+      {
+        "topic": "Machine Learning for Document Classification",
+        "count": 2
+      },
+      {
+        "topic": "Genetic Algorithms for Nutritional Optimization",
+        "count": 2
+      }
+    ],
+    "total_general_unique": 76,
+    "total_specific_unique": 295
+  },
+  "Rizal Setya Perdana": {
+    "general_topics": [
+      {
+        "topic": "Natural Language Processing",
+        "count": 26
+      },
+      {
+        "topic": "Machine Learning",
+        "count": 22
+      },
+      {
+        "topic": "Deep Learning",
+        "count": 15
+      },
+      {
+        "topic": "Computer Vision",
+        "count": 9
+      },
+      {
+        "topic": "Sentiment Analysis",
+        "count": 4
+      },
+      {
+        "topic": "Multimodal Learning",
+        "count": 3
+      },
+      {
+        "topic": "Chatbots",
+        "count": 2
+      },
+      {
+        "topic": "Social Media Analysis",
+        "count": 2
+      },
+      {
+        "topic": "Data Analysis",
+        "count": 2
+      },
+      {
+        "topic": "Augmented Reality",
+        "count": 2
+      }
+    ],
+    "specific_topics": [
+      {
+        "topic": "Natural Language Processing for Named Entity Recognition",
+        "count": 2
+      },
+      {
+        "topic": "Deep Learning for Indonesian Chatbots",
+        "count": 2
+      },
+      {
+        "topic": "Machine Learning for Traffic Congestion Classification",
+        "count": 2
+      },
+      {
+        "topic": "Social Media Analysis for Traffic Monitoring",
+        "count": 2
+      },
+      {
+        "topic": "Natural Language Processing for Sentiment Analysis",
+        "count": 2
+      },
+      {
+        "topic": "Deep Learning for Object Detection",
+        "count": 2
+      },
+      {
+        "topic": "Augmented Reality for 3D Development",
+        "count": 2
+      },
+      {
+        "topic": "Natural Language Processing for Aspect Category Detection",
+        "count": 2
+      },
+      {
+        "topic": "Machine Learning for Restaurant Reviews",
+        "count": 2
+      },
+      {
+        "topic": "Natural Language Processing for Keyword Determination",
+        "count": 2
+      }
+    ],
+    "total_general_unique": 27,
+    "total_specific_unique": 95
+  },
+  "Budi Darma Setiawan": {
+    "general_topics": [
+      {
+        "topic": "Machine Learning",
+        "count": 7
+      },
+      {
+        "topic": "Remote Sensing",
+        "count": 4
+      },
+      {
+        "topic": "Fuzzy Systems",
+        "count": 4
+      },
+      {
+        "topic": "Deep Learning",
+        "count": 3
+      },
+      {
+        "topic": "Computer Vision",
+        "count": 3
+      },
+      {
+        "topic": "Consumer Behavior",
+        "count": 2
+      },
+      {
+        "topic": "Time Series Analysis",
+        "count": 1
+      },
+      {
+        "topic": "Statistical Methods",
+        "count": 1
+      },
+      {
+        "topic": "Financial Technology",
+        "count": 1
+      },
+      {
+        "topic": "Data Augmentation",
+        "count": 1
+      }
+    ],
+    "specific_topics": [
+      {
+        "topic": "Fuzzy Systems for Land Cover Classification",
+        "count": 3
+      },
+      {
+        "topic": "Remote Sensing for UAV Aerial Photography",
+        "count": 3
+      },
+      {
+        "topic": "Time Series Analysis for Tourism Forecasting",
+        "count": 1
+      },
+      {
+        "topic": "Statistical Methods for Tourism Demand Prediction",
+        "count": 1
+      },
+      {
+        "topic": "Machine Learning for Cryptocurrency Prediction",
+        "count": 1
+      },
+      {
+        "topic": "Financial Technology for Bitcoin Value Forecasting",
+        "count": 1
+      },
+      {
+        "topic": "Machine Learning for Road Surface Assessment",
+        "count": 1
+      },
+      {
+        "topic": "Data Augmentation for Sensor Data Analysis",
+        "count": 1
+      },
+      {
+        "topic": "Machine Learning for Road Surface Monitoring",
+        "count": 1
+      },
+      {
+        "topic": "Semantic Segmentation for Motion Sensor Data",
+        "count": 1
+      }
+    ],
+    "total_general_unique": 28,
+    "total_specific_unique": 41
+  },
+  "Edy Santoso": {
+    "general_topics": [
+      {
+        "topic": "Machine Learning",
+        "count": 29
+      },
+      {
+        "topic": "Web Development",
+        "count": 17
+      },
+      {
+        "topic": "Economics",
+        "count": 14
+      },
+      {
+        "topic": "Decision Support Systems",
+        "count": 12
+      },
+      {
+        "topic": "Information Systems",
+        "count": 11
+      },
+      {
+        "topic": "Natural Language Processing",
+        "count": 11
+      },
+      {
+        "topic": "Law",
+        "count": 10
+      },
+      {
+        "topic": "Multi-Criteria Decision Making",
+        "count": 9
+      },
+      {
+        "topic": "Fuzzy Logic",
+        "count": 8
+      },
+      {
+        "topic": "Deep Learning",
+        "count": 8
+      }
+    ],
+    "specific_topics": [
+      {
+        "topic": "Natural Language Processing for Sentiment Analysis",
+        "count": 3
+      },
+      {
+        "topic": "Multi-Criteria Decision Making for Student Selection",
+        "count": 2
+      },
+      {
+        "topic": "Computer Vision for Text Recognition",
+        "count": 2
+      },
+      {
+        "topic": "Deep Learning for Image Processing",
+        "count": 2
+      },
+      {
+        "topic": "Optimization for Extreme Learning Machine",
+        "count": 2
+      },
+      {
+        "topic": "Machine Learning for Land Ownership Prediction",
+        "count": 2
+      },
+      {
+        "topic": "Machine Learning for Cancer Classification",
+        "count": 2
+      },
+      {
+        "topic": "Deep Learning for COVID-19 Prediction",
+        "count": 2
+      },
+      {
+        "topic": "Data Science for Pandemic Variants",
+        "count": 2
+      },
+      {
+        "topic": "E-commerce in Indonesia from a Legal Perspective",
+        "count": 1
+      }
+    ],
+    "total_general_unique": 147,
+    "total_specific_unique": 317
+  },
+  "Marji": {
+    "general_topics": [
+      {
+        "topic": "Machine Learning",
+        "count": 3
+      },
+      {
+        "topic": "Neural Networks",
+        "count": 2
+      },
+      {
+        "topic": "Support Vector Machines",
+        "count": 1
+      },
+      {
+        "topic": "Nutrition",
+        "count": 1
+      },
+      {
+        "topic": "Metaheuristics",
+        "count": 1
+      },
+      {
+        "topic": "Health",
+        "count": 1
+      },
+      {
+        "topic": "Genetic Algorithms",
+        "count": 1
+      }
+    ],
+    "specific_topics": [
+      {
+        "topic": "Machine Learning for Hyperparameter Tuning",
+        "count": 1
+      },
+      {
+        "topic": "Machine Learning for Rainfall Data Classification",
+        "count": 1
+      },
+      {
+        "topic": "Support Vector Machines for Rainfall Data Classification",
+        "count": 1
+      },
+      {
+        "topic": "Neural Networks for Rainfall Data Classification",
+        "count": 1
+      },
+      {
+        "topic": "Nutrition for Pregnant Women",
+        "count": 1
+      },
+      {
+        "topic": "Metaheuristics for Daily Menu Planning",
+        "count": 1
+      },
+      {
+        "topic": "Health for Stunting Prevention",
+        "count": 1
+      },
+      {
+        "topic": "Machine Learning for Hyperparameter Optimization",
+        "count": 1
+      },
+      {
+        "topic": "Neural Networks for Hyperparameter Optimization",
+        "count": 1
+      },
+      {
+        "topic": "Genetic Algorithms for Hyperparameter Optimization",
+        "count": 1
+      }
+    ],
+    "total_general_unique": 7,
+    "total_specific_unique": 10
+  },
+  "Sigit Adinugroho": {
+    "general_topics": [
+      {
+        "topic": "Machine Learning",
+        "count": 46
+      },
+      {
+        "topic": "Natural Language Processing",
+        "count": 18
+      },
+      {
+        "topic": "Computer Vision",
+        "count": 16
+      },
+      {
+        "topic": "Sentiment Analysis",
+        "count": 6
+      },
+      {
+        "topic": "Deep Learning",
+        "count": 6
+      },
+      {
+        "topic": "Medical Imaging",
+        "count": 5
+      },
+      {
+        "topic": "Data Mining",
+        "count": 4
+      },
+      {
+        "topic": "Education",
+        "count": 4
+      },
+      {
+        "topic": "Healthcare",
+        "count": 4
+      },
+      {
+        "topic": "Feature Selection",
+        "count": 4
+      }
+    ],
+    "specific_topics": [
+      {
+        "topic": "Natural Language Processing for Sentiment Analysis",
+        "count": 3
+      },
+      {
+        "topic": "Machine Learning for Naïve Bayes Classification",
+        "count": 3
+      },
+      {
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+      },
+      {
+        "topic": "Information Technology for IT Governance",
+        "count": 3
+      },
+      {
+        "topic": "Information Security for Governance",
+        "count": 3
+      }
+    ],
+    "total_general_unique": 385,
+    "total_specific_unique": 1131
+  },
+  "Lailil Muflikhah": {
+    "general_topics": [
+      {
+        "topic": "Machine Learning",
+        "count": 43
+      },
+      {
+        "topic": "Deep Learning",
+        "count": 20
+      },
+      {
+        "topic": "Bioinformatics",
+        "count": 11
+      },
+      {
+        "topic": "Medical Imaging",
+        "count": 10
+      },
+      {
+        "topic": "Optimization",
+        "count": 9
+      },
+      {
+        "topic": "Natural Language Processing",
+        "count": 8
+      },
+      {
+        "topic": "Computational Biology",
+        "count": 4
+      },
+      {
+        "topic": "Education",
+        "count": 4
+      },
+      {
+        "topic": "Medical Diagnosis",
+        "count": 4
+      },
+      {
+        "topic": "Time Series Analysis",
+        "count": 3
+      }
+    ],
+    "specific_topics": [
+      {
+        "topic": "Deep Learning for Lung Cancer Mutation Detection",
+        "count": 3
+      },
+      {
+        "topic": "Medical Imaging for CT-Scan Analysis",
+        "count": 3
+      },
+      {
+        "topic": "Machine Learning for Rainfall Prediction",
+        "count": 2
+      },
+      {
+        "topic": "Machine Learning for Social Media Analysis",
+        "count": 2
+      },
+      {
+        "topic": "Network Analysis for Consumer Engagement",
+        "count": 2
+      },
+      {
+        "topic": "Machine Learning for Dengue Shock Syndrome Detection",
+        "count": 2
+      },
+      {
+        "topic": "Deep Learning for Stock Price Prediction",
+        "count": 2
+      },
+      {
+        "topic": "Time Series Analysis for Financial Markets",
+        "count": 2
+      },
+      {
+        "topic": "Deep Learning for Alzheimer's Disease Classification",
+        "count": 2
+      },
+      {
+        "topic": "Machine Learning for Disease Classification",
+        "count": 2
+      }
+    ],
+    "total_general_unique": 63,
+    "total_specific_unique": 176
+  },
+  "Fitra Abdurrachman Bachtiar": {
+    "general_topics": [
+      {
+        "topic": "Machine Learning",
+        "count": 52
+      },
+      {
+        "topic": "Deep Learning",
+        "count": 27
+      },
+      {
+        "topic": "Natural Language Processing",
+        "count": 22
+      },
+      {
+        "topic": "Computer Vision",
+        "count": 22
+      },
+      {
+        "topic": "Educational Technology",
+        "count": 10
+      },
+      {
+        "topic": "Gamification",
+        "count": 6
+      },
+      {
+        "topic": "Human-Computer Interaction",
+        "count": 6
+      },
+      {
+        "topic": "Feature Selection",
+        "count": 5
+      },
+      {
+        "topic": "Fuzzy Logic",
+        "count": 5
+      },
+      {
+        "topic": "Clustering",
+        "count": 5
+      }
+    ],
+    "specific_topics": [
+      {
+        "topic": "Natural Language Processing for Sentiment Analysis",
+        "count": 4
+      },
+      {
+        "topic": "Machine Learning for Stress Detection",
+        "count": 3
+      },
+      {
+        "topic": "Deep Learning for Indonesian Language Processing",
+        "count": 2
+      },
+      {
+        "topic": "Computer Vision for Eyeball Movement Detection",
+        "count": 2
+      },
+      {
+        "topic": "Natural Language Processing for Fake Review Detection",
+        "count": 2
+      },
+      {
+        "topic": "Machine Learning for Human Activity Recognition",
+        "count": 2
+      },
+      {
+        "topic": "Machine Learning for Human Activity Classification",
+        "count": 2
+      },
+      {
+        "topic": "Computer Vision for Facial Expression Recognition",
+        "count": 2
+      },
+      {
+        "topic": "Deep Learning for Facial Expression Recognition",
+        "count": 2
+      },
+      {
+        "topic": "Optimization for City Tour Planning",
+        "count": 2
+      }
+    ],
+    "total_general_unique": 104,
+    "total_specific_unique": 274
+  },
+  "Putra Pandu Adikara": {
+    "general_topics": [
+      {
+        "topic": "Machine Learning",
+        "count": 60
+      },
+      {
+        "topic": "Natural Language Processing",
+        "count": 32
+      },
+      {
+        "topic": "Sentiment Analysis",
+        "count": 29
+      },
+      {
+        "topic": "Image Processing",
+        "count": 8
+      },
+      {
+        "topic": "Information Retrieval",
+        "count": 8
+      },
+      {
+        "topic": "Computer Vision",
+        "count": 7
+      },
+      {
+        "topic": "Deep Learning",
+        "count": 6
+      },
+      {
+        "topic": "Text Classification",
+        "count": 5
+      },
+      {
+        "topic": "Neural Networks",
+        "count": 4
+      },
+      {
+        "topic": "Feature Extraction",
+        "count": 4
+      }
+    ],
+    "specific_topics": [
+      {
+        "topic": "Machine Learning for Naïve Bayes Classifier",
+        "count": 4
+      },
+      {
+        "topic": "Machine Learning for Naïve Bayes Classification",
+        "count": 4
+      },
+      {
+        "topic": "Machine Learning for Support Vector Machine (SVM) Classification",
+        "count": 3
+      },
+      {
+        "topic": "Machine Learning for Disease Classification",
+        "count": 3
+      },
+      {
+        "topic": "Machine Learning for Support Vector Machine",
+        "count": 3
+      },
+      {
+        "topic": "Natural Language Processing for Query Expansion",
+        "count": 3
+      },
+      {
+        "topic": "Machine Learning for Maximum Entropy Method",
+        "count": 2
+      },
+      {
+        "topic": "Extreme Learning Machine for Medical Diagnosis",
+        "count": 2
+      },
+      {
+        "topic": "Sentiment Analysis for YouTube Comments",
+        "count": 2
+      },
+      {
+        "topic": "Naive Bayes for Sentiment Classification",
+        "count": 2
+      }
+    ],
+    "total_general_unique": 49,
+    "total_specific_unique": 199
+  },
+  "Dian Eka Ratnawati": {
+    "general_topics": [
+      {
+        "topic": "Machine Learning",
+        "count": 78
+      },
+      {
+        "topic": "Sentiment Analysis",
+        "count": 31
+      },
+      {
+        "topic": "Natural Language Processing",
+        "count": 14
+      },
+      {
+        "topic": "Information Systems",
+        "count": 11
+      },
+      {
+        "topic": "Web Development",
+        "count": 9
+      },
+      {
+        "topic": "Animal Husbandry",
+        "count": 7
+      },
+      {
+        "topic": "Education",
+        "count": 7
+      },
+      {
+        "topic": "Veterinary Science",
+        "count": 6
+      },
+      {
+        "topic": "Cheminformatics",
+        "count": 5
+      },
+      {
+        "topic": "Biology",
+        "count": 5
+      }
+    ],
+    "specific_topics": [
+      {
+        "topic": "Sentiment Analysis for App Reviews",
+        "count": 6
+      },
+      {
+        "topic": "Machine Learning for Sentiment Analysis",
+        "count": 6
+      },
+      {
+        "topic": "Machine Learning for Random Forest",
+        "count": 4
+      },
+      {
+        "topic": "Machine Learning for Support Vector Machine",
+        "count": 3
+      },
+      {
+        "topic": "Biology for Sperm Motility Analysis",
+        "count": 3
+      },
+      {
+        "topic": "Machine Learning for Aspect-Based Sentiment Analysis",
+        "count": 3
+      },
+      {
+        "topic": "Machine Learning for K-Nearest Neighbor",
+        "count": 2
+      },
+      {
+        "topic": "Machine Learning for Random Forest Classifier",
+        "count": 2
+      },
+      {
+        "topic": "Machine Learning for Naïve Bayes Classification",
+        "count": 2
+      },
+      {
+        "topic": "Machine Learning for Chemical Compound Classification",
+        "count": 2
+      }
+    ],
+    "total_general_unique": 83,
+    "total_specific_unique": 249
+  },
+  "Muh Arif Rahman": {
+    "general_topics": [
+      {
+        "topic": "Image Processing",
+        "count": 26
+      },
+      {
+        "topic": "Machine Learning",
+        "count": 18
+      },
+      {
+        "topic": "Computer Vision",
+        "count": 18
+      },
+      {
+        "topic": "Education",
+        "count": 12
+      },
+      {
+        "topic": "Human-Computer Interaction",
+        "count": 6
+      },
+      {
+        "topic": "Information Systems",
+        "count": 5
+      },
+      {
+        "topic": "Genetic Algorithm",
+        "count": 5
+      },
+      {
+        "topic": "Feature Extraction",
+        "count": 4
+      },
+      {
+        "topic": "Signal Processing",
+        "count": 4
+      },
+      {
+        "topic": "Web Development",
+        "count": 4
+      }
+    ],
+    "specific_topics": [
+      {
+        "topic": "Feature Extraction for Local Binary Pattern",
+        "count": 3
+      },
+      {
+        "topic": "Computer Vision for Face Recognition",
+        "count": 3
+      },
+      {
+        "topic": "Computer Vision for Food Image Recognition",
+        "count": 3
+      },
+      {
+        "topic": "Image Processing for Gray Level Co-occurrence Matrix (GLCM)",
+        "count": 2
+      },
+      {
+        "topic": "Education Technology for Online Learning",
+        "count": 2
+      },
+      {
+        "topic": "Image Processing for Fruit Quality Assessment",
+        "count": 2
+      },
+      {
+        "topic": "User Interface Design for Usability Improvement",
+        "count": 2
+      },
+      {
+        "topic": "Image Processing for Road Damage Detection",
+        "count": 2
+      },
+      {
+        "topic": "Computer Vision for Active Contour Models",
+        "count": 2
+      },
+      {
+        "topic": "Image Processing for Attendance Systems",
+        "count": 2
+      }
+    ],
+    "total_general_unique": 120,
+    "total_specific_unique": 235
+  },
+  "Novanto Yudistira": {
+    "general_topics": [
+      {
+        "topic": "Deep Learning",
+        "count": 95
+      },
+      {
+        "topic": "Computer Vision",
+        "count": 45
+      },
+      {
+        "topic": "Machine Learning",
+        "count": 33
+      },
+      {
+        "topic": "Natural Language Processing",
+        "count": 21
+      },
+      {
+        "topic": "Time Series Analysis",
+        "count": 15
+      },
+      {
+        "topic": "Medical Imaging",
+        "count": 7
+      },
+      {
+        "topic": "Transfer Learning",
+        "count": 5
+      },
+      {
+        "topic": "Mobile Development",
+        "count": 5
+      },
+      {
+        "topic": "Generative Models",
+        "count": 3
+      },
+      {
+        "topic": "Image Processing",
+        "count": 3
+      }
+    ],
+    "specific_topics": [
+      {
+        "topic": "Natural Language Processing for Sentiment Analysis",
+        "count": 9
+      },
+      {
+        "topic": "Deep Learning for Time Series Prediction",
+        "count": 5
+      },
+      {
+        "topic": "Deep Learning for Image Classification",
+        "count": 4
+      },
+      {
+        "topic": "Deep Learning for Long Short-Term Memory",
+        "count": 4
+      },
+      {
+        "topic": "Deep Learning for Facial Expression Classification",
+        "count": 4
+      },
+      {
+        "topic": "Deep Learning for Indonesian Language Processing",
+        "count": 2
+      },
+      {
+        "topic": "Natural Language Processing for News Classification",
+        "count": 2
+      },
+      {
+        "topic": "Deep Learning for Medical Image Classification",
+        "count": 2
+      },
+      {
+        "topic": "Computer Vision for Alzheimer's Disease Detection",
+        "count": 2
+      },
+      {
+        "topic": "Deep Learning for Action Recognition",
+        "count": 2
+      }
+    ],
+    "total_general_unique": 62,
+    "total_specific_unique": 254
+  },
+  "Wayan Firdaus Mahmudy": {
+    "general_topics": [
+      {
+        "topic": "Machine Learning",
+        "count": 50
+      },
+      {
+        "topic": "Optimization",
+        "count": 28
+      },
+      {
+        "topic": "Genetic Algorithms",
+        "count": 16
+      },
+      {
+        "topic": "Deep Learning",
+        "count": 12
+      },
+      {
+        "topic": "Agriculture",
+        "count": 11
+      },
+      {
+        "topic": "Computer Vision",
+        "count": 10
+      },
+      {
+        "topic": "Healthcare",
+        "count": 9
+      },
+      {
+        "topic": "Bioinformatics",
+        "count": 6
+      },
+      {
+        "topic": "Neural Networks",
+        "count": 6
+      },
+      {
+        "topic": "Swarm Intelligence",
+        "count": 5
+      }
+    ],
+    "specific_topics": [
+      {
+        "topic": "Machine Learning for Inflation Forecasting",
+        "count": 3
+      },
+      {
+        "topic": "Optimization for Vehicle Routing Problem",
+        "count": 3
+      },
+      {
+        "topic": "Optimization for Production Planning",
+        "count": 3
+      },
+      {
+        "topic": "Economics for Inflation Rate Prediction",
+        "count": 2
+      },
+      {
+        "topic": "Genetic Algorithms for Logistics Optimization",
+        "count": 2
+      },
+      {
+        "topic": "Healthcare for Disease Diagnosis",
+        "count": 2
+      },
+      {
+        "topic": "Neural Networks for Earthquake Prediction",
+        "count": 2
+      },
+      {
+        "topic": "Geophysics for Seismic Activity Analysis",
+        "count": 2
+      },
+      {
+        "topic": "Operations Research for Vehicle Routing",
+        "count": 2
+      },
+      {
+        "topic": "Computer Vision for Plant Disease Detection",
+        "count": 2
+      }
+    ],
+    "total_general_unique": 90,
+    "total_specific_unique": 254
+  }
+}

lecturer_topics_summary.csv

@@ -0,0 +1,29 @@
+Lecturer_Name,General_Topics,Specific_Topics,Total_General_Topics,Total_Specific_Topics,Total_All_Topics
+Arief Andy Soebroto,Machine Learning; Web Development; Information Systems; Decision Support Systems; Artificial Intelligence; Deep Learning; Software Development; Expert Systems; Fuzzy Logic; Agriculture,Machine Learning for Rainfall Forecasting; Decision Support Systems for Early Stroke Detection; Artificial Intelligence for Teaching Materials; Machine Learning for Covid-19 Case Prediction; Optimization Algorithms for Extreme Learning Machine; Fuzzy Logic for Medical Diagnosis; Artificial Intelligence for Theory and Implementation; Machine Learning for Theory and Implementation; Deep Learning for Theory and Implementation; Artificial Intelligence for Overview,88,199,287
+Rekyan Regasari M. P.,Biometrics; Human Resources; Deep Learning; Chemistry,Biometrics for Identity and Data Security; Human Resources for Data Security; Deep Learning for Molecular Structure Classification; Chemistry for Active Compound Function Classification,4,4,8
+Diva Kurnianingtyas,Healthcare; Machine Learning; Deep Learning; Optimization; System Dynamics; Computer Vision; Artificial Intelligence; Natural Language Processing; Swarm Intelligence; Genetic Algorithms,System Dynamics for Financial Strategy; Healthcare for National Health Insurance; Natural Language Processing for Sentiment Analysis; Healthcare for Social Health Insurance; Optimization for Dietary Recommendations; Nutrition for Maternal Health; Blockchain for Food Safety; Risk Management for Halal Certification; Deep Learning for LSTM and Word2Vec; Machine Learning for VADER and Transformers,27,60,87
+Imam Cholissodin,Machine Learning; Optimization; Deep Learning; Natural Language Processing; Genetic Algorithms; Artificial Intelligence; Finance; Algorithm; Bioinformatics; Extreme Learning Machine,Natural Language Processing for Sentiment Analysis; Machine Learning for Disease Classification; Machine Learning for Sentiment Analysis on Twitter; Deep Learning for Cryptocurrency Price Prediction; Time Series Analysis for COVID-19 Case Prediction; Optimization for School Transport Routing; Optimization for School Transportation; Machine Learning for Cancer Classification; Machine Learning for Document Classification; Genetic Algorithms for Nutritional Optimization,76,295,371
+Rizal Setya Perdana,Natural Language Processing; Machine Learning; Deep Learning; Computer Vision; Sentiment Analysis; Multimodal Learning; Chatbots; Social Media Analysis; Data Analysis; Augmented Reality,Natural Language Processing for Named Entity Recognition; Deep Learning for Indonesian Chatbots; Machine Learning for Traffic Congestion Classification; Social Media Analysis for Traffic Monitoring; Natural Language Processing for Sentiment Analysis; Deep Learning for Object Detection; Augmented Reality for 3D Development; Natural Language Processing for Aspect Category Detection; Machine Learning for Restaurant Reviews; Natural Language Processing for Keyword Determination,27,95,122
+Budi Darma Setiawan,Machine Learning; Remote Sensing; Fuzzy Systems; Deep Learning; Computer Vision; Consumer Behavior; Time Series Analysis; Statistical Methods; Financial Technology; Data Augmentation,Fuzzy Systems for Land Cover Classification; Remote Sensing for UAV Aerial Photography; Time Series Analysis for Tourism Forecasting; Statistical Methods for Tourism Demand Prediction; Machine Learning for Cryptocurrency Prediction; Financial Technology for Bitcoin Value Forecasting; Machine Learning for Road Surface Assessment; Data Augmentation for Sensor Data Analysis; Machine Learning for Road Surface Monitoring; Semantic Segmentation for Motion Sensor Data,28,41,69
+Edy Santoso,Machine Learning; Web Development; Economics; Decision Support Systems; Information Systems; Natural Language Processing; Law; Multi-Criteria Decision Making; Fuzzy Logic; Deep Learning,Natural Language Processing for Sentiment Analysis; Multi-Criteria Decision Making for Student Selection; Computer Vision for Text Recognition; Deep Learning for Image Processing; Optimization for Extreme Learning Machine; Machine Learning for Land Ownership Prediction; Machine Learning for Cancer Classification; Deep Learning for COVID-19 Prediction; Data Science for Pandemic Variants; E-commerce in Indonesia from a Legal Perspective,147,317,464
+Marji,Machine Learning; Neural Networks; Support Vector Machines; Nutrition; Metaheuristics; Health; Genetic Algorithms,Machine Learning for Hyperparameter Tuning; Machine Learning for Rainfall Data Classification; Support Vector Machines for Rainfall Data Classification; Neural Networks for Rainfall Data Classification; Nutrition for Pregnant Women; Metaheuristics for Daily Menu Planning; Health for Stunting Prevention; Machine Learning for Hyperparameter Optimization; Neural Networks for Hyperparameter Optimization; Genetic Algorithms for Hyperparameter Optimization,7,10,17
+Sigit Adinugroho,Machine Learning; Natural Language Processing; Computer Vision; Sentiment Analysis; Deep Learning; Medical Imaging; Data Mining; Education; Healthcare; Feature Selection,Natural Language Processing for Sentiment Analysis; Machine Learning for Naïve Bayes Classification; Computer Vision for Image Segmentation; Natural Language Processing for Topic Extraction; Computer Vision for Food Recognition; Natural Language Processing for Social Media Analysis; Natural Language Processing for Text Classification; Machine Learning for Text Classification; Computer Vision for Motion Segmentation; Medical Imaging for Sperm Analysis,46,147,193
+Tirana Noor Fatyanosa,Natural Language Processing; Machine Learning; Deep Learning; Time Series Analysis; Machine Translation; Optimization; Convolutional Neural Networks; Sentiment Analysis; Genetic Algorithms; Computer Vision,Large Language Models for Retrieval Augmented Generation; Machine Learning for Social Media Analysis; Time Series Analysis for Forecasting; Signal Processing for Variational Mode Decomposition; Natural Language Processing for Indonesian Language Resources; Open Source for NLP Resources; Machine Translation for Multilingual Systems; Optimization for Machine Translation Models; Genetic Algorithms for Hyperparameter Tuning; Convolutional Neural Networks for Performance Optimization,27,63,90
+candra dewi,Education; Machine Learning; Pedagogy; Educational Technology; Teaching Methods; Computer Vision; Deep Learning; Optimization; Digital Media; Image Processing,Education for Critical Thinking Skills; Education for Digital Literacy; Education for Mathematics Learning in Elementary School; Education for Problem Based Learning; Digital Media for Elementary Education; Education for Differentiated Learning; Education for Mathematics Learning; Machine Learning for Patchouli Varieties Classification; Optimization for Fuzzy C-Means Clustering; Generative Models for Batik Pattern Generation,99,285,384
+Achmad Ridok,Machine Learning; Sentiment Analysis; Natural Language Processing; Deep Learning; Computer Vision; Education; Bioinformatics; Feature Selection; Transfer Learning; Data Augmentation,Deep Learning for Alzheimer's Disease Classification; Transfer Learning for Medical Image Analysis; Sentiment Analysis for Hospital Services; Machine Learning for Text Classification; Machine Learning for Breast Cancer Detection; Feature Selection for Medical Diagnosis; Computer Vision for Plant Disease Detection; Data Augmentation for Agricultural Applications; Machine Learning for Hepatitis Detection; Feature Selection for Medical Data,34,77,111
+Randy Cahya Wihandika,Machine Learning; Computer Vision; Natural Language Processing; Image Processing; Web Development; Deep Learning; Optimization; Information Systems; Medical Imaging; Regression Analysis,Natural Language Processing for Sentiment Analysis; Computer Vision for Gender Classification; Machine Learning for Population Growth Prediction; Computer Vision for Traditional Food Recognition; Computer Vision for Face Mask Detection; Image Processing for Retinal Blood Vessels; Deep Learning for Emotion Classification; Computer Vision for Facial Feature Analysis; Deep Learning for Pest Classification; Computer Vision for Large Scale Image Classification,45,172,217
+Agus Wahyu Widodo,Image Processing; Medical Imaging; Genetic Algorithms; Agriculture; Deep Learning; Pattern Recognition; Texture Analysis; Wavelet Transform; Remote Sensing; Information Systems,Image Processing for Palm Feature Extraction; Deep Learning for COVID-19 Detection in Chest X-rays; Medical Imaging for COVID-19 Detection; Pattern Recognition for Palm Feature Extraction; Texture Analysis for Palm Feature Extraction; Image Processing for Facial Skin Type Classification; Wavelet Transform for Facial Skin Type Classification; Image Processing for Mangrove Forest Classification; Remote Sensing for Mangrove Forest Classification; Genetic Algorithms for Rice Fertiliser Composition Optimisation,15,21,36
+Bayu Rahayudi,Machine Learning; Optimization; Sentiment Analysis; Web Development; Natural Language Processing; Genetic Algorithms; Computer Vision; Information Systems; E-Commerce; Time Series Analysis,Sentiment Analysis for App Reviews; Machine Learning for User Feedback Analysis; E-Commerce for Payment Gateway Integration; Machine Learning for Naïve Bayes Classification; Time Series Analysis for Sales Prediction; Business Innovation for SMEs; Sentiment Analysis for Hospital Services; Machine Learning for Public Opinion Analysis; Optimization for Distribution Routes; Clustering for Social Welfare Analysis,131,315,446
+Muhammad Tanzil Furqon,Machine Learning; Education; Fuzzy Logic; Natural Language Processing; Time Series Analysis; Cardiology; Medical Research; Neural Networks; Regression Analysis; Decision Support Systems,Machine Learning for Population Growth Prediction; Time Series Analysis for Sales Forecasting; Machine Learning for Feature Extraction; Machine Learning for Sales Prediction; Regression Analysis for Course Enrollment Prediction; Machine Learning for Educational Data; Regression Analysis for Rice Price Prediction; Machine Learning for Agricultural Economics; Big Data for Distance Learning; Educational Technology for Online Learning,84,182,266
+Yuita Arum Sari,Machine Learning; Image Processing; Natural Language Processing; Computer Vision; Deep Learning; Sentiment Analysis; Information Retrieval; Clustering; Feature Selection; Image Classification,Natural Language Processing for Sentiment Analysis; Machine Learning for Sentiment Analysis; Computer Vision for Feature Extraction; Machine Learning for Food Image Classification; Computer Vision for Food Volume Estimation; Machine Learning for Food Image Features; Machine Learning for Food Classification; Image Processing for Food Classification; Image Processing for Traditional Cake Segmentation; Computer Vision for Image Segmentation,70,236,306
+Indriati,Machine Learning; Natural Language Processing; Sentiment Analysis; Information Retrieval; Optimization; Education; Data Mining; Feature Selection; Healthcare; Text Classification,Natural Language Processing for Sentiment Analysis; Machine Learning for Sentiment Analysis; Machine Learning for Improved K-Nearest Neighbor; Information Retrieval for News Articles; Machine Learning for Social Media Analysis; Education for Student Collaboration Skills; Teaching Methods Using Scientific-Based Worksheets; Natural Language Processing for Lexicon-Based Features; Machine Learning for Emotion Detection; Natural Language Processing for Twitter Data,50,236,286
+Irawati Nurmala Sari,Computer Vision; Image Processing; Generative Models; Art Restoration; Augmented Reality; Generative Adversarial Networks; Machine Learning,Computer Vision for Image Inpainting; Computer Vision for Art Painting Completion; Image Processing for Large-Scale Missing Regions; Computer Vision for Painting Completion; Art Restoration for Structure-Texture Consistency; Image Processing for Planar Structure Guidance; Computer Vision for Depth Map Estimation; Augmented Reality for 3D Image Generation; Image Processing for Manhattan World Structures; Generative Adversarial Networks for Edge Enhancement,7,19,26
+Hidayat Nurul,Natural Language Processing; Machine Learning; Hidden Markov Models,Natural Language Processing for Named Entity Recognition; Machine Learning for Medical Herbs Classification; Hidden Markov Models for Text Analysis,3,3,6
+Suprapto,Education; Materials Science; Public Health; Information Technology; Chemistry; Chemical Engineering; Environmental Science; Engineering; Material Science; Nursing,Information Technology for Governance; Human Resources for Employee Performance; COBIT Framework for IT Evaluation; Chemical Engineering for Biodiesel Production; Environmental Science for Dye Adsorption; Chemical Engineering for Waste Utilization; Information Technology for Governance and Risk Management; Information Technology for Risk Management; Information Technology for IT Governance; Information Security for Governance,385,1131,1516
+Lailil Muflikhah,Machine Learning; Deep Learning; Bioinformatics; Medical Imaging; Optimization; Natural Language Processing; Computational Biology; Education; Medical Diagnosis; Time Series Analysis,Deep Learning for Lung Cancer Mutation Detection; Medical Imaging for CT-Scan Analysis; Machine Learning for Rainfall Prediction; Machine Learning for Social Media Analysis; Network Analysis for Consumer Engagement; Machine Learning for Dengue Shock Syndrome Detection; Deep Learning for Stock Price Prediction; Time Series Analysis for Financial Markets; Deep Learning for Alzheimer's Disease Classification; Machine Learning for Disease Classification,63,176,239
+Fitra Abdurrachman Bachtiar,Machine Learning; Deep Learning; Natural Language Processing; Computer Vision; Educational Technology; Gamification; Human-Computer Interaction; Feature Selection; Fuzzy Logic; Clustering,Natural Language Processing for Sentiment Analysis; Machine Learning for Stress Detection; Deep Learning for Indonesian Language Processing; Computer Vision for Eyeball Movement Detection; Natural Language Processing for Fake Review Detection; Machine Learning for Human Activity Recognition; Machine Learning for Human Activity Classification; Computer Vision for Facial Expression Recognition; Deep Learning for Facial Expression Recognition; Optimization for City Tour Planning,104,274,378
+Putra Pandu Adikara,Machine Learning; Natural Language Processing; Sentiment Analysis; Image Processing; Information Retrieval; Computer Vision; Deep Learning; Text Classification; Neural Networks; Feature Extraction,Machine Learning for Naïve Bayes Classifier; Machine Learning for Naïve Bayes Classification; Machine Learning for Support Vector Machine (SVM) Classification; Machine Learning for Disease Classification; Machine Learning for Support Vector Machine; Natural Language Processing for Query Expansion; Machine Learning for Maximum Entropy Method; Extreme Learning Machine for Medical Diagnosis; Sentiment Analysis for YouTube Comments; Naive Bayes for Sentiment Classification,49,199,248
+Dian Eka Ratnawati,Machine Learning; Sentiment Analysis; Natural Language Processing; Information Systems; Web Development; Animal Husbandry; Education; Veterinary Science; Cheminformatics; Biology,Sentiment Analysis for App Reviews; Machine Learning for Sentiment Analysis; Machine Learning for Random Forest; Machine Learning for Support Vector Machine; Biology for Sperm Motility Analysis; Machine Learning for Aspect-Based Sentiment Analysis; Machine Learning for K-Nearest Neighbor; Machine Learning for Random Forest Classifier; Machine Learning for Naïve Bayes Classification; Machine Learning for Chemical Compound Classification,83,249,332
+Muh Arif Rahman,Image Processing; Machine Learning; Computer Vision; Education; Human-Computer Interaction; Information Systems; Genetic Algorithm; Feature Extraction; Signal Processing; Web Development,Feature Extraction for Local Binary Pattern; Computer Vision for Face Recognition; Computer Vision for Food Image Recognition; Image Processing for Gray Level Co-occurrence Matrix (GLCM); Education Technology for Online Learning; Image Processing for Fruit Quality Assessment; User Interface Design for Usability Improvement; Image Processing for Road Damage Detection; Computer Vision for Active Contour Models; Image Processing for Attendance Systems,120,235,355
+Novanto Yudistira,Deep Learning; Computer Vision; Machine Learning; Natural Language Processing; Time Series Analysis; Medical Imaging; Transfer Learning; Mobile Development; Generative Models; Image Processing,Natural Language Processing for Sentiment Analysis; Deep Learning for Time Series Prediction; Deep Learning for Image Classification; Deep Learning for Long Short-Term Memory; Deep Learning for Facial Expression Classification; Deep Learning for Indonesian Language Processing; Natural Language Processing for News Classification; Deep Learning for Medical Image Classification; Computer Vision for Alzheimer's Disease Detection; Deep Learning for Action Recognition,62,254,316
+Wayan Firdaus Mahmudy,Machine Learning; Optimization; Genetic Algorithms; Deep Learning; Agriculture; Computer Vision; Healthcare; Bioinformatics; Neural Networks; Swarm Intelligence,Machine Learning for Inflation Forecasting; Optimization for Vehicle Routing Problem; Optimization for Production Planning; Economics for Inflation Rate Prediction; Genetic Algorithms for Logistics Optimization; Healthcare for Disease Diagnosis; Neural Networks for Earthquake Prediction; Geophysics for Seismic Activity Analysis; Operations Research for Vehicle Routing; Computer Vision for Plant Disease Detection,90,254,344

lecturer_topics_top10.csv

@@ -0,0 +1,29 @@
+Lecturer_Name,Top_General_Topics,Top_Specific_Topics,Top_General_Count,Top_Specific_Count,Total_Unique_General,Total_Unique_Specific
+Arief Andy Soebroto,Machine Learning (22x); Web Development (13x); Information Systems (12x); Decision Support Systems (9x); Artificial Intelligence (8x); Deep Learning (8x); Software Development (8x); Expert Systems (6x); Fuzzy Logic (5x); Agriculture (5x),Machine Learning for Rainfall Forecasting (3x); Decision Support Systems for Early Stroke Detection (3x); Artificial Intelligence for Teaching Materials (2x); Machine Learning for Covid-19 Case Prediction (2x); Optimization Algorithms for Extreme Learning Machine (2x); Fuzzy Logic for Medical Diagnosis (2x); Artificial Intelligence for Theory and Implementation (1x); Machine Learning for Theory and Implementation (1x); Deep Learning for Theory and Implementation (1x); Artificial Intelligence for Overview (1x),10,10,88,199
+Rekyan Regasari M. P.,Biometrics (1x); Human Resources (1x); Deep Learning (1x); Chemistry (1x),Biometrics for Identity and Data Security (1x); Human Resources for Data Security (1x); Deep Learning for Molecular Structure Classification (1x); Chemistry for Active Compound Function Classification (1x),4,4,4,4
+Diva Kurnianingtyas,Healthcare (8x); Machine Learning (7x); Deep Learning (6x); Optimization (6x); System Dynamics (6x); Computer Vision (5x); Artificial Intelligence (4x); Natural Language Processing (3x); Swarm Intelligence (3x); Genetic Algorithms (2x),System Dynamics for Financial Strategy (4x); Healthcare for National Health Insurance (3x); Natural Language Processing for Sentiment Analysis (2x); Healthcare for Social Health Insurance (2x); Optimization for Dietary Recommendations (2x); Nutrition for Maternal Health (2x); Blockchain for Food Safety (2x); Risk Management for Halal Certification (2x); Deep Learning for LSTM and Word2Vec (1x); Machine Learning for VADER and Transformers (1x),10,10,27,60
+Imam Cholissodin,Machine Learning (61x); Optimization (42x); Deep Learning (23x); Natural Language Processing (16x); Genetic Algorithms (15x); Artificial Intelligence (8x); Finance (7x); Algorithm (6x); Bioinformatics (5x); Extreme Learning Machine (5x),Natural Language Processing for Sentiment Analysis (6x); Machine Learning for Disease Classification (3x); Machine Learning for Sentiment Analysis on Twitter (2x); Deep Learning for Cryptocurrency Price Prediction (2x); Time Series Analysis for COVID-19 Case Prediction (2x); Optimization for School Transport Routing (2x); Optimization for School Transportation (2x); Machine Learning for Cancer Classification (2x); Machine Learning for Document Classification (2x); Genetic Algorithms for Nutritional Optimization (2x),10,10,76,295
+Rizal Setya Perdana,Natural Language Processing (26x); Machine Learning (22x); Deep Learning (15x); Computer Vision (9x); Sentiment Analysis (4x); Multimodal Learning (3x); Chatbots (2x); Social Media Analysis (2x); Data Analysis (2x); Augmented Reality (2x),Natural Language Processing for Named Entity Recognition (2x); Deep Learning for Indonesian Chatbots (2x); Machine Learning for Traffic Congestion Classification (2x); Social Media Analysis for Traffic Monitoring (2x); Natural Language Processing for Sentiment Analysis (2x); Deep Learning for Object Detection (2x); Augmented Reality for 3D Development (2x); Natural Language Processing for Aspect Category Detection (2x); Machine Learning for Restaurant Reviews (2x); Natural Language Processing for Keyword Determination (2x),10,10,27,95
+Budi Darma Setiawan,Machine Learning (7x); Remote Sensing (4x); Fuzzy Systems (4x); Deep Learning (3x); Computer Vision (3x); Consumer Behavior (2x); Time Series Analysis (1x); Statistical Methods (1x); Financial Technology (1x); Data Augmentation (1x),Fuzzy Systems for Land Cover Classification (3x); Remote Sensing for UAV Aerial Photography (3x); Time Series Analysis for Tourism Forecasting (1x); Statistical Methods for Tourism Demand Prediction (1x); Machine Learning for Cryptocurrency Prediction (1x); Financial Technology for Bitcoin Value Forecasting (1x); Machine Learning for Road Surface Assessment (1x); Data Augmentation for Sensor Data Analysis (1x); Machine Learning for Road Surface Monitoring (1x); Semantic Segmentation for Motion Sensor Data (1x),10,10,28,41
+Edy Santoso,Machine Learning (29x); Web Development (17x); Economics (14x); Decision Support Systems (12x); Information Systems (11x); Natural Language Processing (11x); Law (10x); Multi-Criteria Decision Making (9x); Fuzzy Logic (8x); Deep Learning (8x),Natural Language Processing for Sentiment Analysis (3x); Multi-Criteria Decision Making for Student Selection (2x); Computer Vision for Text Recognition (2x); Deep Learning for Image Processing (2x); Optimization for Extreme Learning Machine (2x); Machine Learning for Land Ownership Prediction (2x); Machine Learning for Cancer Classification (2x); Deep Learning for COVID-19 Prediction (2x); Data Science for Pandemic Variants (2x); E-commerce in Indonesia from a Legal Perspective (1x),10,10,147,317
+Marji,Machine Learning (3x); Neural Networks (2x); Support Vector Machines (1x); Nutrition (1x); Metaheuristics (1x); Health (1x); Genetic Algorithms (1x),Machine Learning for Hyperparameter Tuning (1x); Machine Learning for Rainfall Data Classification (1x); Support Vector Machines for Rainfall Data Classification (1x); Neural Networks for Rainfall Data Classification (1x); Nutrition for Pregnant Women (1x); Metaheuristics for Daily Menu Planning (1x); Health for Stunting Prevention (1x); Machine Learning for Hyperparameter Optimization (1x); Neural Networks for Hyperparameter Optimization (1x); Genetic Algorithms for Hyperparameter Optimization (1x),7,10,7,10
+Sigit Adinugroho,Machine Learning (46x); Natural Language Processing (18x); Computer Vision (16x); Sentiment Analysis (6x); Deep Learning (6x); Medical Imaging (5x); Data Mining (4x); Education (4x); Healthcare (4x); Feature Selection (4x),Natural Language Processing for Sentiment Analysis (3x); Machine Learning for Naïve Bayes Classification (3x); Computer Vision for Image Segmentation (3x); Natural Language Processing for Topic Extraction (3x); Computer Vision for Food Recognition (2x); Natural Language Processing for Social Media Analysis (2x); Natural Language Processing for Text Classification (2x); Machine Learning for Text Classification (2x); Computer Vision for Motion Segmentation (2x); Medical Imaging for Sperm Analysis (2x),10,10,46,147
+Tirana Noor Fatyanosa,Natural Language Processing (15x); Machine Learning (9x); Deep Learning (4x); Time Series Analysis (4x); Machine Translation (3x); Optimization (3x); Convolutional Neural Networks (3x); Sentiment Analysis (3x); Genetic Algorithms (2x); Computer Vision (2x),Large Language Models for Retrieval Augmented Generation (2x); Machine Learning for Social Media Analysis (2x); Time Series Analysis for Forecasting (2x); Signal Processing for Variational Mode Decomposition (2x); Natural Language Processing for Indonesian Language Resources (1x); Open Source for NLP Resources (1x); Machine Translation for Multilingual Systems (1x); Optimization for Machine Translation Models (1x); Genetic Algorithms for Hyperparameter Tuning (1x); Convolutional Neural Networks for Performance Optimization (1x),10,10,27,63
+candra dewi,Education (64x); Machine Learning (41x); Pedagogy (16x); Educational Technology (15x); Teaching Methods (11x); Computer Vision (10x); Deep Learning (6x); Optimization (6x); Digital Media (5x); Image Processing (5x),Education for Critical Thinking Skills (8x); Education for Digital Literacy (5x); Education for Mathematics Learning in Elementary School (4x); Education for Problem Based Learning (3x); Digital Media for Elementary Education (2x); Education for Differentiated Learning (2x); Education for Mathematics Learning (2x); Machine Learning for Patchouli Varieties Classification (2x); Optimization for Fuzzy C-Means Clustering (2x); Generative Models for Batik Pattern Generation (2x),10,10,99,285
+Achmad Ridok,Machine Learning (19x); Sentiment Analysis (8x); Natural Language Processing (7x); Deep Learning (5x); Computer Vision (4x); Education (4x); Bioinformatics (3x); Feature Selection (2x); Transfer Learning (1x); Data Augmentation (1x),Deep Learning for Alzheimer's Disease Classification (2x); Transfer Learning for Medical Image Analysis (1x); Sentiment Analysis for Hospital Services (1x); Machine Learning for Text Classification (1x); Machine Learning for Breast Cancer Detection (1x); Feature Selection for Medical Diagnosis (1x); Computer Vision for Plant Disease Detection (1x); Data Augmentation for Agricultural Applications (1x); Machine Learning for Hepatitis Detection (1x); Feature Selection for Medical Data (1x),10,10,34,77
+Randy Cahya Wihandika,Machine Learning (45x); Computer Vision (24x); Natural Language Processing (17x); Image Processing (17x); Web Development (9x); Deep Learning (8x); Optimization (5x); Information Systems (5x); Medical Imaging (4x); Regression Analysis (3x),Natural Language Processing for Sentiment Analysis (6x); Computer Vision for Gender Classification (3x); Machine Learning for Population Growth Prediction (2x); Computer Vision for Traditional Food Recognition (2x); Computer Vision for Face Mask Detection (2x); Image Processing for Retinal Blood Vessels (2x); Deep Learning for Emotion Classification (1x); Computer Vision for Facial Feature Analysis (1x); Deep Learning for Pest Classification (1x); Computer Vision for Large Scale Image Classification (1x),10,10,45,172
+Agus Wahyu Widodo,Image Processing (5x); Medical Imaging (2x); Genetic Algorithms (2x); Agriculture (2x); Deep Learning (1x); Pattern Recognition (1x); Texture Analysis (1x); Wavelet Transform (1x); Remote Sensing (1x); Information Systems (1x),Image Processing for Palm Feature Extraction (2x); Deep Learning for COVID-19 Detection in Chest X-rays (1x); Medical Imaging for COVID-19 Detection (1x); Pattern Recognition for Palm Feature Extraction (1x); Texture Analysis for Palm Feature Extraction (1x); Image Processing for Facial Skin Type Classification (1x); Wavelet Transform for Facial Skin Type Classification (1x); Image Processing for Mangrove Forest Classification (1x); Remote Sensing for Mangrove Forest Classification (1x); Genetic Algorithms for Rice Fertiliser Composition Optimisation (1x),10,10,15,21
+Bayu Rahayudi,Machine Learning (51x); Optimization (22x); Sentiment Analysis (19x); Web Development (17x); Natural Language Processing (14x); Genetic Algorithms (12x); Computer Vision (9x); Information Systems (9x); E-Commerce (8x); Time Series Analysis (7x),Sentiment Analysis for App Reviews (4x); Machine Learning for User Feedback Analysis (3x); E-Commerce for Payment Gateway Integration (3x); Machine Learning for Naïve Bayes Classification (3x); Time Series Analysis for Sales Prediction (2x); Business Innovation for SMEs (2x); Sentiment Analysis for Hospital Services (2x); Machine Learning for Public Opinion Analysis (2x); Optimization for Distribution Routes (2x); Clustering for Social Welfare Analysis (2x),10,10,131,315
+Muhammad Tanzil Furqon,Machine Learning (40x); Education (11x); Fuzzy Logic (8x); Natural Language Processing (6x); Time Series Analysis (5x); Cardiology (5x); Medical Research (5x); Neural Networks (4x); Regression Analysis (3x); Decision Support Systems (3x),Machine Learning for Population Growth Prediction (2x); Time Series Analysis for Sales Forecasting (2x); Machine Learning for Feature Extraction (2x); Machine Learning for Sales Prediction (2x); Regression Analysis for Course Enrollment Prediction (1x); Machine Learning for Educational Data (1x); Regression Analysis for Rice Price Prediction (1x); Machine Learning for Agricultural Economics (1x); Big Data for Distance Learning (1x); Educational Technology for Online Learning (1x),10,10,84,182
+Yuita Arum Sari,Machine Learning (51x); Image Processing (34x); Natural Language Processing (29x); Computer Vision (22x); Deep Learning (12x); Sentiment Analysis (10x); Information Retrieval (8x); Clustering (7x); Feature Selection (4x); Image Classification (4x),Natural Language Processing for Sentiment Analysis (7x); Machine Learning for Sentiment Analysis (4x); Computer Vision for Feature Extraction (4x); Machine Learning for Food Image Classification (3x); Computer Vision for Food Volume Estimation (3x); Machine Learning for Food Image Features (3x); Machine Learning for Food Classification (3x); Image Processing for Food Classification (2x); Image Processing for Traditional Cake Segmentation (2x); Computer Vision for Image Segmentation (2x),10,10,70,236
+Indriati,Machine Learning (83x); Natural Language Processing (53x); Sentiment Analysis (29x); Information Retrieval (23x); Optimization (6x); Education (5x); Data Mining (5x); Feature Selection (5x); Healthcare (4x); Text Classification (4x),Natural Language Processing for Sentiment Analysis (8x); Machine Learning for Sentiment Analysis (5x); Machine Learning for Improved K-Nearest Neighbor (4x); Information Retrieval for News Articles (3x); Machine Learning for Social Media Analysis (3x); Education for Student Collaboration Skills (2x); Teaching Methods Using Scientific-Based Worksheets (2x); Natural Language Processing for Lexicon-Based Features (2x); Machine Learning for Emotion Detection (2x); Natural Language Processing for Twitter Data (2x),10,10,50,236
+Irawati Nurmala Sari,Computer Vision (11x); Image Processing (7x); Generative Models (2x); Art Restoration (1x); Augmented Reality (1x); Generative Adversarial Networks (1x); Machine Learning (1x),Computer Vision for Image Inpainting (5x); Computer Vision for Art Painting Completion (2x); Image Processing for Large-Scale Missing Regions (1x); Computer Vision for Painting Completion (1x); Art Restoration for Structure-Texture Consistency (1x); Image Processing for Planar Structure Guidance (1x); Computer Vision for Depth Map Estimation (1x); Augmented Reality for 3D Image Generation (1x); Image Processing for Manhattan World Structures (1x); Generative Adversarial Networks for Edge Enhancement (1x),7,10,7,19
+Hidayat Nurul,Natural Language Processing (1x); Machine Learning (1x); Hidden Markov Models (1x),Natural Language Processing for Named Entity Recognition (1x); Machine Learning for Medical Herbs Classification (1x); Hidden Markov Models for Text Analysis (1x),3,3,3,3
+Suprapto,Education (79x); Materials Science (49x); Public Health (43x); Information Technology (41x); Chemistry (33x); Chemical Engineering (29x); Environmental Science (26x); Engineering (23x); Material Science (21x); Nursing (20x),Information Technology for Governance (10x); Human Resources for Employee Performance (5x); COBIT Framework for IT Evaluation (5x); Chemical Engineering for Biodiesel Production (4x); Environmental Science for Dye Adsorption (3x); Chemical Engineering for Waste Utilization (3x); Information Technology for Governance and Risk Management (3x); Information Technology for Risk Management (3x); Information Technology for IT Governance (3x); Information Security for Governance (3x),10,10,385,1131
+Lailil Muflikhah,Machine Learning (43x); Deep Learning (20x); Bioinformatics (11x); Medical Imaging (10x); Optimization (9x); Natural Language Processing (8x); Computational Biology (4x); Education (4x); Medical Diagnosis (4x); Time Series Analysis (3x),Deep Learning for Lung Cancer Mutation Detection (3x); Medical Imaging for CT-Scan Analysis (3x); Machine Learning for Rainfall Prediction (2x); Machine Learning for Social Media Analysis (2x); Network Analysis for Consumer Engagement (2x); Machine Learning for Dengue Shock Syndrome Detection (2x); Deep Learning for Stock Price Prediction (2x); Time Series Analysis for Financial Markets (2x); Deep Learning for Alzheimer's Disease Classification (2x); Machine Learning for Disease Classification (2x),10,10,63,176
+Fitra Abdurrachman Bachtiar,Machine Learning (52x); Deep Learning (27x); Natural Language Processing (22x); Computer Vision (22x); Educational Technology (10x); Gamification (6x); Human-Computer Interaction (6x); Feature Selection (5x); Fuzzy Logic (5x); Clustering (5x),Natural Language Processing for Sentiment Analysis (4x); Machine Learning for Stress Detection (3x); Deep Learning for Indonesian Language Processing (2x); Computer Vision for Eyeball Movement Detection (2x); Natural Language Processing for Fake Review Detection (2x); Machine Learning for Human Activity Recognition (2x); Machine Learning for Human Activity Classification (2x); Computer Vision for Facial Expression Recognition (2x); Deep Learning for Facial Expression Recognition (2x); Optimization for City Tour Planning (2x),10,10,104,274
+Putra Pandu Adikara,Machine Learning (60x); Natural Language Processing (32x); Sentiment Analysis (29x); Image Processing (8x); Information Retrieval (8x); Computer Vision (7x); Deep Learning (6x); Text Classification (5x); Neural Networks (4x); Feature Extraction (4x),Machine Learning for Naïve Bayes Classifier (4x); Machine Learning for Naïve Bayes Classification (4x); Machine Learning for Support Vector Machine (SVM) Classification (3x); Machine Learning for Disease Classification (3x); Machine Learning for Support Vector Machine (3x); Natural Language Processing for Query Expansion (3x); Machine Learning for Maximum Entropy Method (2x); Extreme Learning Machine for Medical Diagnosis (2x); Sentiment Analysis for YouTube Comments (2x); Naive Bayes for Sentiment Classification (2x),10,10,49,199
+Dian Eka Ratnawati,Machine Learning (78x); Sentiment Analysis (31x); Natural Language Processing (14x); Information Systems (11x); Web Development (9x); Animal Husbandry (7x); Education (7x); Veterinary Science (6x); Cheminformatics (5x); Biology (5x),Sentiment Analysis for App Reviews (6x); Machine Learning for Sentiment Analysis (6x); Machine Learning for Random Forest (4x); Machine Learning for Support Vector Machine (3x); Biology for Sperm Motility Analysis (3x); Machine Learning for Aspect-Based Sentiment Analysis (3x); Machine Learning for K-Nearest Neighbor (2x); Machine Learning for Random Forest Classifier (2x); Machine Learning for Naïve Bayes Classification (2x); Machine Learning for Chemical Compound Classification (2x),10,10,83,249
+Muh Arif Rahman,Image Processing (26x); Machine Learning (18x); Computer Vision (18x); Education (12x); Human-Computer Interaction (6x); Information Systems (5x); Genetic Algorithm (5x); Feature Extraction (4x); Signal Processing (4x); Web Development (4x),Feature Extraction for Local Binary Pattern (3x); Computer Vision for Face Recognition (3x); Computer Vision for Food Image Recognition (3x); Image Processing for Gray Level Co-occurrence Matrix (GLCM) (2x); Education Technology for Online Learning (2x); Image Processing for Fruit Quality Assessment (2x); User Interface Design for Usability Improvement (2x); Image Processing for Road Damage Detection (2x); Computer Vision for Active Contour Models (2x); Image Processing for Attendance Systems (2x),10,10,120,235
+Novanto Yudistira,Deep Learning (95x); Computer Vision (45x); Machine Learning (33x); Natural Language Processing (21x); Time Series Analysis (15x); Medical Imaging (7x); Transfer Learning (5x); Mobile Development (5x); Generative Models (3x); Image Processing (3x),Natural Language Processing for Sentiment Analysis (9x); Deep Learning for Time Series Prediction (5x); Deep Learning for Image Classification (4x); Deep Learning for Long Short-Term Memory (4x); Deep Learning for Facial Expression Classification (4x); Deep Learning for Indonesian Language Processing (2x); Natural Language Processing for News Classification (2x); Deep Learning for Medical Image Classification (2x); Computer Vision for Alzheimer's Disease Detection (2x); Deep Learning for Action Recognition (2x),10,10,62,254
+Wayan Firdaus Mahmudy,Machine Learning (50x); Optimization (28x); Genetic Algorithms (16x); Deep Learning (12x); Agriculture (11x); Computer Vision (10x); Healthcare (9x); Bioinformatics (6x); Neural Networks (6x); Swarm Intelligence (5x),Machine Learning for Inflation Forecasting (3x); Optimization for Vehicle Routing Problem (3x); Optimization for Production Planning (3x); Economics for Inflation Rate Prediction (2x); Genetic Algorithms for Logistics Optimization (2x); Healthcare for Disease Diagnosis (2x); Neural Networks for Earthquake Prediction (2x); Geophysics for Seismic Activity Analysis (2x); Operations Research for Vehicle Routing (2x); Computer Vision for Plant Disease Detection (2x),10,10,90,254

lecturer_topics_top10_detailed.csv

@@ -0,0 +1,529 @@
+Lecturer_Name,Topic_Type,Rank,Topic,Frequency
+Arief Andy Soebroto,General,1,Machine Learning,22
+Arief Andy Soebroto,General,2,Web Development,13
+Arief Andy Soebroto,General,3,Information Systems,12
+Arief Andy Soebroto,General,4,Decision Support Systems,9
+Arief Andy Soebroto,General,5,Artificial Intelligence,8
+Arief Andy Soebroto,General,6,Deep Learning,8
+Arief Andy Soebroto,General,7,Software Development,8
+Arief Andy Soebroto,General,8,Expert Systems,6
+Arief Andy Soebroto,General,9,Fuzzy Logic,5
+Arief Andy Soebroto,General,10,Agriculture,5
+Arief Andy Soebroto,Specific,1,Machine Learning for Rainfall Forecasting,3
+Arief Andy Soebroto,Specific,2,Decision Support Systems for Early Stroke Detection,3
+Arief Andy Soebroto,Specific,3,Artificial Intelligence for Teaching Materials,2
+Arief Andy Soebroto,Specific,4,Machine Learning for Covid-19 Case Prediction,2
+Arief Andy Soebroto,Specific,5,Optimization Algorithms for Extreme Learning Machine,2
+Arief Andy Soebroto,Specific,6,Fuzzy Logic for Medical Diagnosis,2
+Arief Andy Soebroto,Specific,7,Artificial Intelligence for Theory and Implementation,1
+Arief Andy Soebroto,Specific,8,Machine Learning for Theory and Implementation,1
+Arief Andy Soebroto,Specific,9,Deep Learning for Theory and Implementation,1
+Arief Andy Soebroto,Specific,10,Artificial Intelligence for Overview,1
+Rekyan Regasari M. P.,General,1,Biometrics,1
+Rekyan Regasari M. P.,General,2,Human Resources,1
+Rekyan Regasari M. P.,General,3,Deep Learning,1
+Rekyan Regasari M. P.,General,4,Chemistry,1
+Rekyan Regasari M. P.,Specific,1,Biometrics for Identity and Data Security,1
+Rekyan Regasari M. P.,Specific,2,Human Resources for Data Security,1
+Rekyan Regasari M. P.,Specific,3,Deep Learning for Molecular Structure Classification,1
+Rekyan Regasari M. P.,Specific,4,Chemistry for Active Compound Function Classification,1
+Diva Kurnianingtyas,General,1,Healthcare,8
+Diva Kurnianingtyas,General,2,Machine Learning,7
+Diva Kurnianingtyas,General,3,Deep Learning,6
+Diva Kurnianingtyas,General,4,Optimization,6
+Diva Kurnianingtyas,General,5,System Dynamics,6
+Diva Kurnianingtyas,General,6,Computer Vision,5
+Diva Kurnianingtyas,General,7,Artificial Intelligence,4
+Diva Kurnianingtyas,General,8,Natural Language Processing,3
+Diva Kurnianingtyas,General,9,Swarm Intelligence,3
+Diva Kurnianingtyas,General,10,Genetic Algorithms,2
+Diva Kurnianingtyas,Specific,1,System Dynamics for Financial Strategy,4
+Diva Kurnianingtyas,Specific,2,Healthcare for National Health Insurance,3
+Diva Kurnianingtyas,Specific,3,Natural Language Processing for Sentiment Analysis,2
+Diva Kurnianingtyas,Specific,4,Healthcare for Social Health Insurance,2
+Diva Kurnianingtyas,Specific,5,Optimization for Dietary Recommendations,2
+Diva Kurnianingtyas,Specific,6,Nutrition for Maternal Health,2
+Diva Kurnianingtyas,Specific,7,Blockchain for Food Safety,2
+Diva Kurnianingtyas,Specific,8,Risk Management for Halal Certification,2
+Diva Kurnianingtyas,Specific,9,Deep Learning for LSTM and Word2Vec,1
+Diva Kurnianingtyas,Specific,10,Machine Learning for VADER and Transformers,1
+Imam Cholissodin,General,1,Machine Learning,61
+Imam Cholissodin,General,2,Optimization,42
+Imam Cholissodin,General,3,Deep Learning,23
+Imam Cholissodin,General,4,Natural Language Processing,16
+Imam Cholissodin,General,5,Genetic Algorithms,15
+Imam Cholissodin,General,6,Artificial Intelligence,8
+Imam Cholissodin,General,7,Finance,7
+Imam Cholissodin,General,8,Algorithm,6
+Imam Cholissodin,General,9,Bioinformatics,5
+Imam Cholissodin,General,10,Extreme Learning Machine,5
+Imam Cholissodin,Specific,1,Natural Language Processing for Sentiment Analysis,6
+Imam Cholissodin,Specific,2,Machine Learning for Disease Classification,3
+Imam Cholissodin,Specific,3,Machine Learning for Sentiment Analysis on Twitter,2
+Imam Cholissodin,Specific,4,Deep Learning for Cryptocurrency Price Prediction,2
+Imam Cholissodin,Specific,5,Time Series Analysis for COVID-19 Case Prediction,2
+Imam Cholissodin,Specific,6,Optimization for School Transport Routing,2
+Imam Cholissodin,Specific,7,Optimization for School Transportation,2
+Imam Cholissodin,Specific,8,Machine Learning for Cancer Classification,2
+Imam Cholissodin,Specific,9,Machine Learning for Document Classification,2
+Imam Cholissodin,Specific,10,Genetic Algorithms for Nutritional Optimization,2
+Rizal Setya Perdana,General,1,Natural Language Processing,26
+Rizal Setya Perdana,General,2,Machine Learning,22
+Rizal Setya Perdana,General,3,Deep Learning,15
+Rizal Setya Perdana,General,4,Computer Vision,9
+Rizal Setya Perdana,General,5,Sentiment Analysis,4
+Rizal Setya Perdana,General,6,Multimodal Learning,3
+Rizal Setya Perdana,General,7,Chatbots,2
+Rizal Setya Perdana,General,8,Social Media Analysis,2
+Rizal Setya Perdana,General,9,Data Analysis,2
+Rizal Setya Perdana,General,10,Augmented Reality,2
+Rizal Setya Perdana,Specific,1,Natural Language Processing for Named Entity Recognition,2
+Rizal Setya Perdana,Specific,2,Deep Learning for Indonesian Chatbots,2
+Rizal Setya Perdana,Specific,3,Machine Learning for Traffic Congestion Classification,2
+Rizal Setya Perdana,Specific,4,Social Media Analysis for Traffic Monitoring,2
+Rizal Setya Perdana,Specific,5,Natural Language Processing for Sentiment Analysis,2
+Rizal Setya Perdana,Specific,6,Deep Learning for Object Detection,2
+Rizal Setya Perdana,Specific,7,Augmented Reality for 3D Development,2
+Rizal Setya Perdana,Specific,8,Natural Language Processing for Aspect Category Detection,2
+Rizal Setya Perdana,Specific,9,Machine Learning for Restaurant Reviews,2
+Rizal Setya Perdana,Specific,10,Natural Language Processing for Keyword Determination,2
+Budi Darma Setiawan,General,1,Machine Learning,7
+Budi Darma Setiawan,General,2,Remote Sensing,4
+Budi Darma Setiawan,General,3,Fuzzy Systems,4
+Budi Darma Setiawan,General,4,Deep Learning,3
+Budi Darma Setiawan,General,5,Computer Vision,3
+Budi Darma Setiawan,General,6,Consumer Behavior,2
+Budi Darma Setiawan,General,7,Time Series Analysis,1
+Budi Darma Setiawan,General,8,Statistical Methods,1
+Budi Darma Setiawan,General,9,Financial Technology,1
+Budi Darma Setiawan,General,10,Data Augmentation,1
+Budi Darma Setiawan,Specific,1,Fuzzy Systems for Land Cover Classification,3
+Budi Darma Setiawan,Specific,2,Remote Sensing for UAV Aerial Photography,3
+Budi Darma Setiawan,Specific,3,Time Series Analysis for Tourism Forecasting,1
+Budi Darma Setiawan,Specific,4,Statistical Methods for Tourism Demand Prediction,1
+Budi Darma Setiawan,Specific,5,Machine Learning for Cryptocurrency Prediction,1
+Budi Darma Setiawan,Specific,6,Financial Technology for Bitcoin Value Forecasting,1
+Budi Darma Setiawan,Specific,7,Machine Learning for Road Surface Assessment,1
+Budi Darma Setiawan,Specific,8,Data Augmentation for Sensor Data Analysis,1
+Budi Darma Setiawan,Specific,9,Machine Learning for Road Surface Monitoring,1
+Budi Darma Setiawan,Specific,10,Semantic Segmentation for Motion Sensor Data,1
+Edy Santoso,General,1,Machine Learning,29
+Edy Santoso,General,2,Web Development,17
+Edy Santoso,General,3,Economics,14
+Edy Santoso,General,4,Decision Support Systems,12
+Edy Santoso,General,5,Information Systems,11
+Edy Santoso,General,6,Natural Language Processing,11
+Edy Santoso,General,7,Law,10
+Edy Santoso,General,8,Multi-Criteria Decision Making,9
+Edy Santoso,General,9,Fuzzy Logic,8
+Edy Santoso,General,10,Deep Learning,8
+Edy Santoso,Specific,1,Natural Language Processing for Sentiment Analysis,3
+Edy Santoso,Specific,2,Multi-Criteria Decision Making for Student Selection,2
+Edy Santoso,Specific,3,Computer Vision for Text Recognition,2
+Edy Santoso,Specific,4,Deep Learning for Image Processing,2
+Edy Santoso,Specific,5,Optimization for Extreme Learning Machine,2
+Edy Santoso,Specific,6,Machine Learning for Land Ownership Prediction,2
+Edy Santoso,Specific,7,Machine Learning for Cancer Classification,2
+Edy Santoso,Specific,8,Deep Learning for COVID-19 Prediction,2
+Edy Santoso,Specific,9,Data Science for Pandemic Variants,2
+Edy Santoso,Specific,10,E-commerce in Indonesia from a Legal Perspective,1
+Marji,General,1,Machine Learning,3
+Marji,General,2,Neural Networks,2
+Marji,General,3,Support Vector Machines,1
+Marji,General,4,Nutrition,1
+Marji,General,5,Metaheuristics,1
+Marji,General,6,Health,1
+Marji,General,7,Genetic Algorithms,1
+Marji,Specific,1,Machine Learning for Hyperparameter Tuning,1
+Marji,Specific,2,Machine Learning for Rainfall Data Classification,1
+Marji,Specific,3,Support Vector Machines for Rainfall Data Classification,1
+Marji,Specific,4,Neural Networks for Rainfall Data Classification,1
+Marji,Specific,5,Nutrition for Pregnant Women,1
+Marji,Specific,6,Metaheuristics for Daily Menu Planning,1
+Marji,Specific,7,Health for Stunting Prevention,1
+Marji,Specific,8,Machine Learning for Hyperparameter Optimization,1
+Marji,Specific,9,Neural Networks for Hyperparameter Optimization,1
+Marji,Specific,10,Genetic Algorithms for Hyperparameter Optimization,1
+Sigit Adinugroho,General,1,Machine Learning,46
+Sigit Adinugroho,General,2,Natural Language Processing,18
+Sigit Adinugroho,General,3,Computer Vision,16
+Sigit Adinugroho,General,4,Sentiment Analysis,6
+Sigit Adinugroho,General,5,Deep Learning,6
+Sigit Adinugroho,General,6,Medical Imaging,5
+Sigit Adinugroho,General,7,Data Mining,4
+Sigit Adinugroho,General,8,Education,4
+Sigit Adinugroho,General,9,Healthcare,4
+Sigit Adinugroho,General,10,Feature Selection,4
+Sigit Adinugroho,Specific,1,Natural Language Processing for Sentiment Analysis,3
+Sigit Adinugroho,Specific,2,Machine Learning for Naïve Bayes Classification,3
+Sigit Adinugroho,Specific,3,Computer Vision for Image Segmentation,3
+Sigit Adinugroho,Specific,4,Natural Language Processing for Topic Extraction,3
+Sigit Adinugroho,Specific,5,Computer Vision for Food Recognition,2
+Sigit Adinugroho,Specific,6,Natural Language Processing for Social Media Analysis,2
+Sigit Adinugroho,Specific,7,Natural Language Processing for Text Classification,2
+Sigit Adinugroho,Specific,8,Machine Learning for Text Classification,2
+Sigit Adinugroho,Specific,9,Computer Vision for Motion Segmentation,2
+Sigit Adinugroho,Specific,10,Medical Imaging for Sperm Analysis,2
+Tirana Noor Fatyanosa,General,1,Natural Language Processing,15
+Tirana Noor Fatyanosa,General,2,Machine Learning,9
+Tirana Noor Fatyanosa,General,3,Deep Learning,4
+Tirana Noor Fatyanosa,General,4,Time Series Analysis,4
+Tirana Noor Fatyanosa,General,5,Machine Translation,3
+Tirana Noor Fatyanosa,General,6,Optimization,3
+Tirana Noor Fatyanosa,General,7,Convolutional Neural Networks,3
+Tirana Noor Fatyanosa,General,8,Sentiment Analysis,3
+Tirana Noor Fatyanosa,General,9,Genetic Algorithms,2
+Tirana Noor Fatyanosa,General,10,Computer Vision,2
+Tirana Noor Fatyanosa,Specific,1,Large Language Models for Retrieval Augmented Generation,2
+Tirana Noor Fatyanosa,Specific,2,Machine Learning for Social Media Analysis,2
+Tirana Noor Fatyanosa,Specific,3,Time Series Analysis for Forecasting,2
+Tirana Noor Fatyanosa,Specific,4,Signal Processing for Variational Mode Decomposition,2
+Tirana Noor Fatyanosa,Specific,5,Natural Language Processing for Indonesian Language Resources,1
+Tirana Noor Fatyanosa,Specific,6,Open Source for NLP Resources,1
+Tirana Noor Fatyanosa,Specific,7,Machine Translation for Multilingual Systems,1
+Tirana Noor Fatyanosa,Specific,8,Optimization for Machine Translation Models,1
+Tirana Noor Fatyanosa,Specific,9,Genetic Algorithms for Hyperparameter Tuning,1
+Tirana Noor Fatyanosa,Specific,10,Convolutional Neural Networks for Performance Optimization,1
+candra dewi,General,1,Education,64
+candra dewi,General,2,Machine Learning,41
+candra dewi,General,3,Pedagogy,16
+candra dewi,General,4,Educational Technology,15
+candra dewi,General,5,Teaching Methods,11
+candra dewi,General,6,Computer Vision,10
+candra dewi,General,7,Deep Learning,6
+candra dewi,General,8,Optimization,6
+candra dewi,General,9,Digital Media,5
+candra dewi,General,10,Image Processing,5
+candra dewi,Specific,1,Education for Critical Thinking Skills,8
+candra dewi,Specific,2,Education for Digital Literacy,5
+candra dewi,Specific,3,Education for Mathematics Learning in Elementary School,4
+candra dewi,Specific,4,Education for Problem Based Learning,3
+candra dewi,Specific,5,Digital Media for Elementary Education,2
+candra dewi,Specific,6,Education for Differentiated Learning,2
+candra dewi,Specific,7,Education for Mathematics Learning,2
+candra dewi,Specific,8,Machine Learning for Patchouli Varieties Classification,2
+candra dewi,Specific,9,Optimization for Fuzzy C-Means Clustering,2
+candra dewi,Specific,10,Generative Models for Batik Pattern Generation,2
+Achmad Ridok,General,1,Machine Learning,19
+Achmad Ridok,General,2,Sentiment Analysis,8
+Achmad Ridok,General,3,Natural Language Processing,7
+Achmad Ridok,General,4,Deep Learning,5
+Achmad Ridok,General,5,Computer Vision,4
+Achmad Ridok,General,6,Education,4
+Achmad Ridok,General,7,Bioinformatics,3
+Achmad Ridok,General,8,Feature Selection,2
+Achmad Ridok,General,9,Transfer Learning,1
+Achmad Ridok,General,10,Data Augmentation,1
+Achmad Ridok,Specific,1,Deep Learning for Alzheimer's Disease Classification,2
+Achmad Ridok,Specific,2,Transfer Learning for Medical Image Analysis,1
+Achmad Ridok,Specific,3,Sentiment Analysis for Hospital Services,1
+Achmad Ridok,Specific,4,Machine Learning for Text Classification,1
+Achmad Ridok,Specific,5,Machine Learning for Breast Cancer Detection,1
+Achmad Ridok,Specific,6,Feature Selection for Medical Diagnosis,1
+Achmad Ridok,Specific,7,Computer Vision for Plant Disease Detection,1
+Achmad Ridok,Specific,8,Data Augmentation for Agricultural Applications,1
+Achmad Ridok,Specific,9,Machine Learning for Hepatitis Detection,1
+Achmad Ridok,Specific,10,Feature Selection for Medical Data,1
+Randy Cahya Wihandika,General,1,Machine Learning,45
+Randy Cahya Wihandika,General,2,Computer Vision,24
+Randy Cahya Wihandika,General,3,Natural Language Processing,17
+Randy Cahya Wihandika,General,4,Image Processing,17
+Randy Cahya Wihandika,General,5,Web Development,9
+Randy Cahya Wihandika,General,6,Deep Learning,8
+Randy Cahya Wihandika,General,7,Optimization,5
+Randy Cahya Wihandika,General,8,Information Systems,5
+Randy Cahya Wihandika,General,9,Medical Imaging,4
+Randy Cahya Wihandika,General,10,Regression Analysis,3
+Randy Cahya Wihandika,Specific,1,Natural Language Processing for Sentiment Analysis,6
+Randy Cahya Wihandika,Specific,2,Computer Vision for Gender Classification,3
+Randy Cahya Wihandika,Specific,3,Machine Learning for Population Growth Prediction,2
+Randy Cahya Wihandika,Specific,4,Computer Vision for Traditional Food Recognition,2
+Randy Cahya Wihandika,Specific,5,Computer Vision for Face Mask Detection,2
+Randy Cahya Wihandika,Specific,6,Image Processing for Retinal Blood Vessels,2
+Randy Cahya Wihandika,Specific,7,Deep Learning for Emotion Classification,1
+Randy Cahya Wihandika,Specific,8,Computer Vision for Facial Feature Analysis,1
+Randy Cahya Wihandika,Specific,9,Deep Learning for Pest Classification,1
+Randy Cahya Wihandika,Specific,10,Computer Vision for Large Scale Image Classification,1
+Agus Wahyu Widodo,General,1,Image Processing,5
+Agus Wahyu Widodo,General,2,Medical Imaging,2
+Agus Wahyu Widodo,General,3,Genetic Algorithms,2
+Agus Wahyu Widodo,General,4,Agriculture,2
+Agus Wahyu Widodo,General,5,Deep Learning,1
+Agus Wahyu Widodo,General,6,Pattern Recognition,1
+Agus Wahyu Widodo,General,7,Texture Analysis,1
+Agus Wahyu Widodo,General,8,Wavelet Transform,1
+Agus Wahyu Widodo,General,9,Remote Sensing,1
+Agus Wahyu Widodo,General,10,Information Systems,1
+Agus Wahyu Widodo,Specific,1,Image Processing for Palm Feature Extraction,2
+Agus Wahyu Widodo,Specific,2,Deep Learning for COVID-19 Detection in Chest X-rays,1
+Agus Wahyu Widodo,Specific,3,Medical Imaging for COVID-19 Detection,1
+Agus Wahyu Widodo,Specific,4,Pattern Recognition for Palm Feature Extraction,1
+Agus Wahyu Widodo,Specific,5,Texture Analysis for Palm Feature Extraction,1
+Agus Wahyu Widodo,Specific,6,Image Processing for Facial Skin Type Classification,1
+Agus Wahyu Widodo,Specific,7,Wavelet Transform for Facial Skin Type Classification,1
+Agus Wahyu Widodo,Specific,8,Image Processing for Mangrove Forest Classification,1
+Agus Wahyu Widodo,Specific,9,Remote Sensing for Mangrove Forest Classification,1
+Agus Wahyu Widodo,Specific,10,Genetic Algorithms for Rice Fertiliser Composition Optimisation,1
+Bayu Rahayudi,General,1,Machine Learning,51
+Bayu Rahayudi,General,2,Optimization,22
+Bayu Rahayudi,General,3,Sentiment Analysis,19
+Bayu Rahayudi,General,4,Web Development,17
+Bayu Rahayudi,General,5,Natural Language Processing,14
+Bayu Rahayudi,General,6,Genetic Algorithms,12
+Bayu Rahayudi,General,7,Computer Vision,9
+Bayu Rahayudi,General,8,Information Systems,9
+Bayu Rahayudi,General,9,E-Commerce,8
+Bayu Rahayudi,General,10,Time Series Analysis,7
+Bayu Rahayudi,Specific,1,Sentiment Analysis for App Reviews,4
+Bayu Rahayudi,Specific,2,Machine Learning for User Feedback Analysis,3
+Bayu Rahayudi,Specific,3,E-Commerce for Payment Gateway Integration,3
+Bayu Rahayudi,Specific,4,Machine Learning for Naïve Bayes Classification,3
+Bayu Rahayudi,Specific,5,Time Series Analysis for Sales Prediction,2
+Bayu Rahayudi,Specific,6,Business Innovation for SMEs,2
+Bayu Rahayudi,Specific,7,Sentiment Analysis for Hospital Services,2
+Bayu Rahayudi,Specific,8,Machine Learning for Public Opinion Analysis,2
+Bayu Rahayudi,Specific,9,Optimization for Distribution Routes,2
+Bayu Rahayudi,Specific,10,Clustering for Social Welfare Analysis,2
+Muhammad Tanzil Furqon,General,1,Machine Learning,40
+Muhammad Tanzil Furqon,General,2,Education,11
+Muhammad Tanzil Furqon,General,3,Fuzzy Logic,8
+Muhammad Tanzil Furqon,General,4,Natural Language Processing,6
+Muhammad Tanzil Furqon,General,5,Time Series Analysis,5
+Muhammad Tanzil Furqon,General,6,Cardiology,5
+Muhammad Tanzil Furqon,General,7,Medical Research,5
+Muhammad Tanzil Furqon,General,8,Neural Networks,4
+Muhammad Tanzil Furqon,General,9,Regression Analysis,3
+Muhammad Tanzil Furqon,General,10,Decision Support Systems,3
+Muhammad Tanzil Furqon,Specific,1,Machine Learning for Population Growth Prediction,2
+Muhammad Tanzil Furqon,Specific,2,Time Series Analysis for Sales Forecasting,2
+Muhammad Tanzil Furqon,Specific,3,Machine Learning for Feature Extraction,2
+Muhammad Tanzil Furqon,Specific,4,Machine Learning for Sales Prediction,2
+Muhammad Tanzil Furqon,Specific,5,Regression Analysis for Course Enrollment Prediction,1
+Muhammad Tanzil Furqon,Specific,6,Machine Learning for Educational Data,1
+Muhammad Tanzil Furqon,Specific,7,Regression Analysis for Rice Price Prediction,1
+Muhammad Tanzil Furqon,Specific,8,Machine Learning for Agricultural Economics,1
+Muhammad Tanzil Furqon,Specific,9,Big Data for Distance Learning,1
+Muhammad Tanzil Furqon,Specific,10,Educational Technology for Online Learning,1
+Yuita Arum Sari,General,1,Machine Learning,51
+Yuita Arum Sari,General,2,Image Processing,34
+Yuita Arum Sari,General,3,Natural Language Processing,29
+Yuita Arum Sari,General,4,Computer Vision,22
+Yuita Arum Sari,General,5,Deep Learning,12
+Yuita Arum Sari,General,6,Sentiment Analysis,10
+Yuita Arum Sari,General,7,Information Retrieval,8
+Yuita Arum Sari,General,8,Clustering,7
+Yuita Arum Sari,General,9,Feature Selection,4
+Yuita Arum Sari,General,10,Image Classification,4
+Yuita Arum Sari,Specific,1,Natural Language Processing for Sentiment Analysis,7
+Yuita Arum Sari,Specific,2,Machine Learning for Sentiment Analysis,4
+Yuita Arum Sari,Specific,3,Computer Vision for Feature Extraction,4
+Yuita Arum Sari,Specific,4,Machine Learning for Food Image Classification,3
+Yuita Arum Sari,Specific,5,Computer Vision for Food Volume Estimation,3
+Yuita Arum Sari,Specific,6,Machine Learning for Food Image Features,3
+Yuita Arum Sari,Specific,7,Machine Learning for Food Classification,3
+Yuita Arum Sari,Specific,8,Image Processing for Food Classification,2
+Yuita Arum Sari,Specific,9,Image Processing for Traditional Cake Segmentation,2
+Yuita Arum Sari,Specific,10,Computer Vision for Image Segmentation,2
+Indriati,General,1,Machine Learning,83
+Indriati,General,2,Natural Language Processing,53
+Indriati,General,3,Sentiment Analysis,29
+Indriati,General,4,Information Retrieval,23
+Indriati,General,5,Optimization,6
+Indriati,General,6,Education,5
+Indriati,General,7,Data Mining,5
+Indriati,General,8,Feature Selection,5
+Indriati,General,9,Healthcare,4
+Indriati,General,10,Text Classification,4
+Indriati,Specific,1,Natural Language Processing for Sentiment Analysis,8
+Indriati,Specific,2,Machine Learning for Sentiment Analysis,5
+Indriati,Specific,3,Machine Learning for Improved K-Nearest Neighbor,4
+Indriati,Specific,4,Information Retrieval for News Articles,3
+Indriati,Specific,5,Machine Learning for Social Media Analysis,3
+Indriati,Specific,6,Education for Student Collaboration Skills,2
+Indriati,Specific,7,Teaching Methods Using Scientific-Based Worksheets,2
+Indriati,Specific,8,Natural Language Processing for Lexicon-Based Features,2
+Indriati,Specific,9,Machine Learning for Emotion Detection,2
+Indriati,Specific,10,Natural Language Processing for Twitter Data,2
+Irawati Nurmala Sari,General,1,Computer Vision,11
+Irawati Nurmala Sari,General,2,Image Processing,7
+Irawati Nurmala Sari,General,3,Generative Models,2
+Irawati Nurmala Sari,General,4,Art Restoration,1
+Irawati Nurmala Sari,General,5,Augmented Reality,1
+Irawati Nurmala Sari,General,6,Generative Adversarial Networks,1
+Irawati Nurmala Sari,General,7,Machine Learning,1
+Irawati Nurmala Sari,Specific,1,Computer Vision for Image Inpainting,5
+Irawati Nurmala Sari,Specific,2,Computer Vision for Art Painting Completion,2
+Irawati Nurmala Sari,Specific,3,Image Processing for Large-Scale Missing Regions,1
+Irawati Nurmala Sari,Specific,4,Computer Vision for Painting Completion,1
+Irawati Nurmala Sari,Specific,5,Art Restoration for Structure-Texture Consistency,1
+Irawati Nurmala Sari,Specific,6,Image Processing for Planar Structure Guidance,1
+Irawati Nurmala Sari,Specific,7,Computer Vision for Depth Map Estimation,1
+Irawati Nurmala Sari,Specific,8,Augmented Reality for 3D Image Generation,1
+Irawati Nurmala Sari,Specific,9,Image Processing for Manhattan World Structures,1
+Irawati Nurmala Sari,Specific,10,Generative Adversarial Networks for Edge Enhancement,1
+Hidayat Nurul,General,1,Natural Language Processing,1
+Hidayat Nurul,General,2,Machine Learning,1
+Hidayat Nurul,General,3,Hidden Markov Models,1
+Hidayat Nurul,Specific,1,Natural Language Processing for Named Entity Recognition,1
+Hidayat Nurul,Specific,2,Machine Learning for Medical Herbs Classification,1
+Hidayat Nurul,Specific,3,Hidden Markov Models for Text Analysis,1
+Suprapto,General,1,Education,79
+Suprapto,General,2,Materials Science,49
+Suprapto,General,3,Public Health,43
+Suprapto,General,4,Information Technology,41
+Suprapto,General,5,Chemistry,33
+Suprapto,General,6,Chemical Engineering,29
+Suprapto,General,7,Environmental Science,26
+Suprapto,General,8,Engineering,23
+Suprapto,General,9,Material Science,21
+Suprapto,General,10,Nursing,20
+Suprapto,Specific,1,Information Technology for Governance,10
+Suprapto,Specific,2,Human Resources for Employee Performance,5
+Suprapto,Specific,3,COBIT Framework for IT Evaluation,5
+Suprapto,Specific,4,Chemical Engineering for Biodiesel Production,4
+Suprapto,Specific,5,Environmental Science for Dye Adsorption,3
+Suprapto,Specific,6,Chemical Engineering for Waste Utilization,3
+Suprapto,Specific,7,Information Technology for Governance and Risk Management,3
+Suprapto,Specific,8,Information Technology for Risk Management,3
+Suprapto,Specific,9,Information Technology for IT Governance,3
+Suprapto,Specific,10,Information Security for Governance,3
+Lailil Muflikhah,General,1,Machine Learning,43
+Lailil Muflikhah,General,2,Deep Learning,20
+Lailil Muflikhah,General,3,Bioinformatics,11
+Lailil Muflikhah,General,4,Medical Imaging,10
+Lailil Muflikhah,General,5,Optimization,9
+Lailil Muflikhah,General,6,Natural Language Processing,8
+Lailil Muflikhah,General,7,Computational Biology,4
+Lailil Muflikhah,General,8,Education,4
+Lailil Muflikhah,General,9,Medical Diagnosis,4
+Lailil Muflikhah,General,10,Time Series Analysis,3
+Lailil Muflikhah,Specific,1,Deep Learning for Lung Cancer Mutation Detection,3
+Lailil Muflikhah,Specific,2,Medical Imaging for CT-Scan Analysis,3
+Lailil Muflikhah,Specific,3,Machine Learning for Rainfall Prediction,2
+Lailil Muflikhah,Specific,4,Machine Learning for Social Media Analysis,2
+Lailil Muflikhah,Specific,5,Network Analysis for Consumer Engagement,2
+Lailil Muflikhah,Specific,6,Machine Learning for Dengue Shock Syndrome Detection,2
+Lailil Muflikhah,Specific,7,Deep Learning for Stock Price Prediction,2
+Lailil Muflikhah,Specific,8,Time Series Analysis for Financial Markets,2
+Lailil Muflikhah,Specific,9,Deep Learning for Alzheimer's Disease Classification,2
+Lailil Muflikhah,Specific,10,Machine Learning for Disease Classification,2
+Fitra Abdurrachman Bachtiar,General,1,Machine Learning,52
+Fitra Abdurrachman Bachtiar,General,2,Deep Learning,27
+Fitra Abdurrachman Bachtiar,General,3,Natural Language Processing,22
+Fitra Abdurrachman Bachtiar,General,4,Computer Vision,22
+Fitra Abdurrachman Bachtiar,General,5,Educational Technology,10
+Fitra Abdurrachman Bachtiar,General,6,Gamification,6
+Fitra Abdurrachman Bachtiar,General,7,Human-Computer Interaction,6
+Fitra Abdurrachman Bachtiar,General,8,Feature Selection,5
+Fitra Abdurrachman Bachtiar,General,9,Fuzzy Logic,5
+Fitra Abdurrachman Bachtiar,General,10,Clustering,5
+Fitra Abdurrachman Bachtiar,Specific,1,Natural Language Processing for Sentiment Analysis,4
+Fitra Abdurrachman Bachtiar,Specific,2,Machine Learning for Stress Detection,3
+Fitra Abdurrachman Bachtiar,Specific,3,Deep Learning for Indonesian Language Processing,2
+Fitra Abdurrachman Bachtiar,Specific,4,Computer Vision for Eyeball Movement Detection,2
+Fitra Abdurrachman Bachtiar,Specific,5,Natural Language Processing for Fake Review Detection,2
+Fitra Abdurrachman Bachtiar,Specific,6,Machine Learning for Human Activity Recognition,2
+Fitra Abdurrachman Bachtiar,Specific,7,Machine Learning for Human Activity Classification,2
+Fitra Abdurrachman Bachtiar,Specific,8,Computer Vision for Facial Expression Recognition,2
+Fitra Abdurrachman Bachtiar,Specific,9,Deep Learning for Facial Expression Recognition,2
+Fitra Abdurrachman Bachtiar,Specific,10,Optimization for City Tour Planning,2
+Putra Pandu Adikara,General,1,Machine Learning,60
+Putra Pandu Adikara,General,2,Natural Language Processing,32
+Putra Pandu Adikara,General,3,Sentiment Analysis,29
+Putra Pandu Adikara,General,4,Image Processing,8
+Putra Pandu Adikara,General,5,Information Retrieval,8
+Putra Pandu Adikara,General,6,Computer Vision,7
+Putra Pandu Adikara,General,7,Deep Learning,6
+Putra Pandu Adikara,General,8,Text Classification,5
+Putra Pandu Adikara,General,9,Neural Networks,4
+Putra Pandu Adikara,General,10,Feature Extraction,4
+Putra Pandu Adikara,Specific,1,Machine Learning for Naïve Bayes Classifier,4
+Putra Pandu Adikara,Specific,2,Machine Learning for Naïve Bayes Classification,4
+Putra Pandu Adikara,Specific,3,Machine Learning for Support Vector Machine (SVM) Classification,3
+Putra Pandu Adikara,Specific,4,Machine Learning for Disease Classification,3
+Putra Pandu Adikara,Specific,5,Machine Learning for Support Vector Machine,3
+Putra Pandu Adikara,Specific,6,Natural Language Processing for Query Expansion,3
+Putra Pandu Adikara,Specific,7,Machine Learning for Maximum Entropy Method,2
+Putra Pandu Adikara,Specific,8,Extreme Learning Machine for Medical Diagnosis,2
+Putra Pandu Adikara,Specific,9,Sentiment Analysis for YouTube Comments,2
+Putra Pandu Adikara,Specific,10,Naive Bayes for Sentiment Classification,2
+Dian Eka Ratnawati,General,1,Machine Learning,78
+Dian Eka Ratnawati,General,2,Sentiment Analysis,31
+Dian Eka Ratnawati,General,3,Natural Language Processing,14
+Dian Eka Ratnawati,General,4,Information Systems,11
+Dian Eka Ratnawati,General,5,Web Development,9
+Dian Eka Ratnawati,General,6,Animal Husbandry,7
+Dian Eka Ratnawati,General,7,Education,7
+Dian Eka Ratnawati,General,8,Veterinary Science,6
+Dian Eka Ratnawati,General,9,Cheminformatics,5
+Dian Eka Ratnawati,General,10,Biology,5
+Dian Eka Ratnawati,Specific,1,Sentiment Analysis for App Reviews,6
+Dian Eka Ratnawati,Specific,2,Machine Learning for Sentiment Analysis,6
+Dian Eka Ratnawati,Specific,3,Machine Learning for Random Forest,4
+Dian Eka Ratnawati,Specific,4,Machine Learning for Support Vector Machine,3
+Dian Eka Ratnawati,Specific,5,Biology for Sperm Motility Analysis,3
+Dian Eka Ratnawati,Specific,6,Machine Learning for Aspect-Based Sentiment Analysis,3
+Dian Eka Ratnawati,Specific,7,Machine Learning for K-Nearest Neighbor,2
+Dian Eka Ratnawati,Specific,8,Machine Learning for Random Forest Classifier,2
+Dian Eka Ratnawati,Specific,9,Machine Learning for Naïve Bayes Classification,2
+Dian Eka Ratnawati,Specific,10,Machine Learning for Chemical Compound Classification,2
+Muh Arif Rahman,General,1,Image Processing,26
+Muh Arif Rahman,General,2,Machine Learning,18
+Muh Arif Rahman,General,3,Computer Vision,18
+Muh Arif Rahman,General,4,Education,12
+Muh Arif Rahman,General,5,Human-Computer Interaction,6
+Muh Arif Rahman,General,6,Information Systems,5
+Muh Arif Rahman,General,7,Genetic Algorithm,5
+Muh Arif Rahman,General,8,Feature Extraction,4
+Muh Arif Rahman,General,9,Signal Processing,4
+Muh Arif Rahman,General,10,Web Development,4
+Muh Arif Rahman,Specific,1,Feature Extraction for Local Binary Pattern,3
+Muh Arif Rahman,Specific,2,Computer Vision for Face Recognition,3
+Muh Arif Rahman,Specific,3,Computer Vision for Food Image Recognition,3
+Muh Arif Rahman,Specific,4,Image Processing for Gray Level Co-occurrence Matrix (GLCM),2
+Muh Arif Rahman,Specific,5,Education Technology for Online Learning,2
+Muh Arif Rahman,Specific,6,Image Processing for Fruit Quality Assessment,2
+Muh Arif Rahman,Specific,7,User Interface Design for Usability Improvement,2
+Muh Arif Rahman,Specific,8,Image Processing for Road Damage Detection,2
+Muh Arif Rahman,Specific,9,Computer Vision for Active Contour Models,2
+Muh Arif Rahman,Specific,10,Image Processing for Attendance Systems,2
+Novanto Yudistira,General,1,Deep Learning,95
+Novanto Yudistira,General,2,Computer Vision,45
+Novanto Yudistira,General,3,Machine Learning,33
+Novanto Yudistira,General,4,Natural Language Processing,21
+Novanto Yudistira,General,5,Time Series Analysis,15
+Novanto Yudistira,General,6,Medical Imaging,7
+Novanto Yudistira,General,7,Transfer Learning,5
+Novanto Yudistira,General,8,Mobile Development,5
+Novanto Yudistira,General,9,Generative Models,3
+Novanto Yudistira,General,10,Image Processing,3
+Novanto Yudistira,Specific,1,Natural Language Processing for Sentiment Analysis,9
+Novanto Yudistira,Specific,2,Deep Learning for Time Series Prediction,5
+Novanto Yudistira,Specific,3,Deep Learning for Image Classification,4
+Novanto Yudistira,Specific,4,Deep Learning for Long Short-Term Memory,4
+Novanto Yudistira,Specific,5,Deep Learning for Facial Expression Classification,4
+Novanto Yudistira,Specific,6,Deep Learning for Indonesian Language Processing,2
+Novanto Yudistira,Specific,7,Natural Language Processing for News Classification,2
+Novanto Yudistira,Specific,8,Deep Learning for Medical Image Classification,2
+Novanto Yudistira,Specific,9,Computer Vision for Alzheimer's Disease Detection,2
+Novanto Yudistira,Specific,10,Deep Learning for Action Recognition,2
+Wayan Firdaus Mahmudy,General,1,Machine Learning,50
+Wayan Firdaus Mahmudy,General,2,Optimization,28
+Wayan Firdaus Mahmudy,General,3,Genetic Algorithms,16
+Wayan Firdaus Mahmudy,General,4,Deep Learning,12
+Wayan Firdaus Mahmudy,General,5,Agriculture,11
+Wayan Firdaus Mahmudy,General,6,Computer Vision,10
+Wayan Firdaus Mahmudy,General,7,Healthcare,9
+Wayan Firdaus Mahmudy,General,8,Bioinformatics,6
+Wayan Firdaus Mahmudy,General,9,Neural Networks,6
+Wayan Firdaus Mahmudy,General,10,Swarm Intelligence,5
+Wayan Firdaus Mahmudy,Specific,1,Machine Learning for Inflation Forecasting,3
+Wayan Firdaus Mahmudy,Specific,2,Optimization for Vehicle Routing Problem,3
+Wayan Firdaus Mahmudy,Specific,3,Optimization for Production Planning,3
+Wayan Firdaus Mahmudy,Specific,4,Economics for Inflation Rate Prediction,2
+Wayan Firdaus Mahmudy,Specific,5,Genetic Algorithms for Logistics Optimization,2
+Wayan Firdaus Mahmudy,Specific,6,Healthcare for Disease Diagnosis,2
+Wayan Firdaus Mahmudy,Specific,7,Neural Networks for Earthquake Prediction,2
+Wayan Firdaus Mahmudy,Specific,8,Geophysics for Seismic Activity Analysis,2
+Wayan Firdaus Mahmudy,Specific,9,Operations Research for Vehicle Routing,2
+Wayan Firdaus Mahmudy,Specific,10,Computer Vision for Plant Disease Detection,2

publications/publications_budi_darma_setiawan.csv

@@ -0,0 +1,46 @@
+Author_Name,Author_Interests,Publication_Title,Year,Citations,Journal,Authors,Publication_Date,Profile_URL,Abstract
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Perbandingan Double Moving Average dan Double Exponential Smoothing untuk Peramalan Jumlah Kedatangan Wisatawan Mancanegara di Bandara Ngurah Rai,2019,96,Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer,"Cinthia Vairra Hudiyanti, Fitra Abdurrachman Bachtiar, Budi Darma Setiawan",2019/1/11,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Setiap tahunnya jumlah kedatangan mancanegara di Bali selalu meningkat (BPS, Badan Pusat Statistik). Peningkatan jumlah kedatangan mancanegara ini akan berdampak pada kesediaan sarana, prasarana, maupun layanan pihak bandara atau Angkasa Pura I. Banyak hal mempengaruhi kedatangan mancanegara, yang mengakibatkan dibutuhkannya peramalan jumlah kedatangan mancanegara yang hasilnya dapat digunakan oleh pihak Angkasa Pura I sebagai pihak pengelola bandara dan pemerintah daerah untuk meningkatkan pelayanan. Penelitian ini peramalan dilakukan menggunakan Double Moving Average dan Double Exponential Smoothing. Perhitungan akurasi dilakukan dengan menggunakan Mean Absoulte Percentage Error (MAPE). Data yang digunakan sebanyak 120 data yaitu dari bulan Januari 2008 hingga Desember 2017, dan didapatkan dari situs resmi Badan Pusat Statistik. Dari penelitian ini pengujian pada tahun 2017 didapatkan nilai orde waktu terbaik untuk Double Moving Average adalah 2 dan Double Exponential Smoothing dengan parameter ð› ¼= 0.4. Dari nilai parameter tersebut didapatkan nilai MAPE Double Moving Average sebesar 10,522 dan nilai MAPE Double Exponential Smoothing sebesar 3,355. Pada Double Exponential Smoothing memiliki nilai dibawah 10 maka dikatakan sangat baik, sedangkan Double Moving Average dengan nilai diatas 10 dikatakan baik. Dapat disimpulkan bahwa Double Exponential Smoothing memiliki akurasi lebih baik dibandingkan Double Moving Average pada peramalan jumlah kedatangan wisatawan mancanegara di Bandara Ngurah Rai."
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Automatic Essay Scoring System Using N-Gram and Cosine Similarity for Gamification Based E-Learning,2017,61,,"M Ali Fauzi, Djoko Cahyo Utomo, Budi Darma Setiawan, Eko Sakti Pramukantoro",2017/8/25,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"E-Learning is one of the great innovations in teaching methods. In the E-learning, there are several assessment methods; one of them is the essay examination. Essay assessment takes a long time if corrected manually. Therefore, researches on automatic essay scoring have been growing rapidly in recent years. The method that is usually used for automatic essay scoring is Cosine Similarity by utilizing bag of words as the feature extraction. However, the feature extraction by using bag of words did not consider to the order of words in a sentence. Meanwhile, the order of words in an essay has an important role in the assessment. In this study, an automatic essay scoring system based on n-gram and cosine similarity was proposed. N-gram was used for feature extraction and modified to split by word instead of by letter so that the word order would be considered. Based on evaluation results, this system got the best …"
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,"Perbandingan Holt's dan Winter's Exponential Smoothing untuk Peramalan Indeks Harga Konsumen Kelompok Transportasi, Komunikasi dan Jasa Keuangan",2018,33,Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer,"Achmad Fahlevi, Fitra Abdurrachman Bachtiar, Budi Darma Setiawan",2018/8/6,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Indeks Harga Konsumen (IHK) merupakan salah satu indikator yang paling sering digunakan untuk mengukur tingkat inflasi. Kelompok Transportasi, Komunikasi dan Jasa Keuangan merupakan kelompok IHK yang memiliki proporsi biaya hidup tertinggi ke dua sebesar 19.15%. Sebagai kelompok yang tergolong sebagai administered price (harga yang diatur pemerintah), peramalan ini membantu pihak-pihak terkait untuk mengambil keputusan terkait untuk menghindari inflasi yang terlalu tinggi. Peramalan pada penelitian ini dilakukan menggunakan dua metode Exponential Smoothing, yaitu Holt's Exponential Smoothing dan Winters Exponential Smoothing. Evaluasi hasil peramalan dilakukan dengan menghitung nilai rata-rata error menggunakan metode Mean Absoulte Percentage Error (MAPE). Data yang digunakan sebanyak 120 data dari bulan Januari 2007 hingga Desember 2017, dan didapatkan dari situs resmi Bank Indonesia (www. bi. go. id). Dari penelitian ini didapatkan nilai parameter paling optimal dari Holt's Exponential Smoothing yaitu ð› ¼= 0.7 dan β= 0.1 dan Winters Exponential Smoothing yaitu ð› ¼= 0.1, β= 0.4 dan γ= 0.8. Kemudian dengan nilai parameter tersebut didapatkan nilai MAPE dari Holt's Exponential Smoothing sebesar 0.474% dan nilai MAPE dari Winters Exponential Smoothing sebesar 1.503%. Keduanya memiliki nilai MAPE dibawah 10% sehingga dapat diklasifikasikan sebagai sangat baik untuk meramalkan IHK kelompok Transportasi, Komunikasi dan Jasa Keuangan. Dapat disimpulkan juga bahwa Holt's Exponential Smoothing memiliki akurasi yang lebih baik dibandingkan Winters Exponential …"
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Lake edge detection using Canny algorithm and Otsu thresholding,2017,31,,"Budi Darma Setiawan, Alfi Nur Rusydi, Koko Pradityo",2017/11/24,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Change that is happened to the lake can have implication to its surrounding. Monitoring the change can be done remotely by using remote sensing images. To do it automatically, we first need to detect the lake region in the images, then gain the edges between water and land area. This research aims to implement the Canny edge detection method, combining with Otsu thresholding to detect the edges. Otsu thresholding is used to gain threshold value for Canny Method. In result, some edges are well detected, but some others are missed. Some of the false detected edges are gained from thresholding process, where shadow or dark pixel area which have nearly same color as water, are also detected as water."
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Prediksi Nilai Cryptocurrency Bitcoin menggunakan Algoritme Extreme Learning Machine (ELM),2019,28,Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer,"Rahmat Faizal, Budi Darma Setiawan, Imam Cholisoddin",2019/3/22,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Bitcoin merupakan salah satu bentuk cryptocurrency yang dilirik masyarakat karena pengelolaannya yang terdesentraliasi, kerahasiaan yang terjaga, serta prosesnya yang mudah. Namun, mata uang digital ini mengalami fluktuasi yang ekstrim yang membuat beberapa pemilik aset merasa dirugikan. Banyak cara dilakukan untuk mencegahnya seperti melihat pergerakan nilai secara terus menerus yang, melakukan aksi tanpa mempertimbangkan prospek kedepannya, atau bahkan membiarkannya sampai waktu yang ditentukan oleh pemilik aset. Tentu saja hal itu tidak efisien mengingat tujuan utama penyimpanan aset adalah mendapatkan keuntungan. Maka dari itu, diperlukan sistem yang dapat memprediksi nilai Bitcoin secara tepat dan akurat sehingga dapat membantu mengurangi kerugian serta menjadi bahan pertimbangan dalam proses jual beli cryptocurrency Bitcoin. Penelitian ini memiliki tujuan untuk mendapatkan nilai prediksi cryptocurrency Bitcoin menggunakan algoritme Extreme Learning Machine (ELM). Berdasarkan hasil implementasi serta analisis yang telah dilakukan menggunakan data Bitcoin dari tanggal 1 Mei 2018 sampai dengan 1 Agustus 2018 diperoleh nilai kesalahan terkecil menggunakan Mean Absolute Percentage Error (MAPE) sebesar 2,657% dengan jumlah fitur sebanyak 2, jumlah hidden neuron sebanyak 4, persentase jumlah data latih sebesar 80%, serta rentang nilai bobot dengan rentang [-1.8, 1.8]."
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Comparative Analysis of K-Means and Isodata Algorithms for Clustering of Fire Point Data in Sumatra Region,2018,15,,"Edo Fadila Sirat, Budi Darma Setiawan, Fatwa Ramdani",2018/11/10,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Forest, land, or residential fire is a familiar phenomenon in Indonesia for last decade. The high number of fire incidents in Indonesia requires attention from the government so that any natural disasters such as forest fires can be resolved. These fire incidents can be analyzed since the data has already been obtained and recorded from satellite. Unfortunately, the data is too large to be analyzed as it was. Based on data obtained from the EOSDIS website, recorded as many as 289,256 fire spots occur in the region of Sumatra in the timeframe between 2001 and 2014. It needs an algorithm to segment the data or clusters the data so that large data can be processed into good information for the user. In this study, a comparative study of clustering algorithms between the K-Means and the Isodata was conducted. Both algorithms used in this study were assessed based on the quality of the clusters produced, which is …"
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Prediksi harga saham berdasarkan data historis menggunakan model regresi yang dibangun dengan algoritma genetika',2015,15,DORO: Repository Jurnal Mahasiswa PTIIK Universitas Brawijaya,"Asyrofa Rahmi, Wayan Firdaus Mahmudy, Budi Darma Setiawan",2015,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Jual beli saham merupakan hal yang sangat menarik. Karena saham bisa membuat para investor memperoleh keuntungan yang besar namun bisa sebaliknya. Untuk mendapatkan keuntungan yang besar, investor perlu melakukan analisa dalam memprediksi harga saham. Namun, memprediksi harga saham adalah hal yang sulit dilakukan karena harga saham mengalami fluktuasi setiap waktu dengan cepat. Sehingga investor perlu memprediksi harga saham sesingkat mungkin. Salah satu teknik yang dapat dipakai untuk memprediksi adalah menggunakan pendekatan Algoritma Genetika. Algoritma Genetika sendiri memiliki ruang pencarian yang sangat luas sehingga bisa mendapatkan solusi terbaik untuk berbagai macam permasalahan. Dalam mengimplementasikan algoritma genetika ini, representasi kromosom yang digunakan adalah real coded, proses crossover yang digunakan adalah extended intermadiate, random mutation pada proses mutasi dan metode seleksi replacement selection. Dari hasil pengujian yang dilakukan, sistem mampu menghasilkan prediksi terbaik pada ukuran terbaik populasi 1200, generasi terbaik sebanyak 1500, kombinasi terbaik cr 0,5 dan mr 0,5 serta periode saham terbaik pada 5 hari. Prediksi terbaik dibuktikan dari nilai MSE terkecil 47,5023 yang didapatkan oleh harga prediksi hasil perhitungan Algoritma Genetika. Hal ini membuktikan bahwa koefisien (kromosom) terbaik hasil perhitungan Algoritma Genetika tersebut dapat digunakan untuk memprediksi harga saham di masa mendatang dengan lebih baik dibandingkan dengan koefisien hasil perhitungan manual regresi dengan aplikasi MiniTab."
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Smartphone Sensor Data Augmentation for Automatic Road Surface Assessment Using a Small Training Dataset,2021,13,,"Budi Darma Setiawan, Uwe Imre Serdült, Victor Kryssanov",2021/1/17,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Smartphones equipped with motion sensors are widely used for data collection in research aimed at the establishment of smart transportation and at, more specifically, automatic road condition assessment. To perform the assessment task, machine learning classifier systems are developed to analyze patterns of vibration signals recorded from a driver's smartphone. Obtaining a balanced training dataset required for the classifier system to work properly is, however, a difficult task. The presented study develops an approach based on an Unrolled Generative Adversarial Network (Unrolled GAN) to produce synthetic data for balancing the training dataset. Experiments conducted in the study demonstrated that the approach allows for generating high-quality synthetic data as long as the unrolled GAN are kept controlled to balance the discriminator and generator modules of the networks."
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Semantic Segmentation on Smartphone Motion Sensor Data for Road Surface Monitoring,2022,11,Procedia Computer Science,"Budi Darma Setiawan, Mate Kovacs, Uwe Serdült, Victor Kryssanov",2022/1/1,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Improving road safety is one of the critical issues for road maintenance and management. Motion sensors embedded in smartphones to sense vibrations can be used to detect rough road surfaces when carried in moving vehicles. Finding segments in the signal which reflect the condition of the road surface, however, is a challenging task. This study proposes a modified U-Net architecture with integrated bidirectional Long Short-Term Memory layers to perform semantic segmentation on smartphone motion sensor data for road surface classification. Experiments show that using z-axis accelerometer and z-axis gyroscope features, the proposed method outperforms multiple existing semantic segmentation algorithms."
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Sistem Pendukung Keputusan Seleksi Penerimaan Asisten Praktikum Menggunakan Metode Profile Matching,2013,11,Repository Jurnal Mahasiswa PTIIK UB,"Kusumaning Hati Pambayun, Raden Arief Setyawan, Budi Darma Setiawan",2013,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Asisten praktikum di Program Studi Teknik Informatika Universitas Brawijaya adalah mahasiswa yang dipilih sebagai asisten tenaga pengajar kegiatan praktikum melalui proses seleksi. Proses penilaian dan pengambilan keputusan dalam seleksi biasanya bersifat subjektif terutama jika ada beberapa calon asisten yang memiliki kemampuan yang tidak jauh berbeda. Aplikasi yang dibuat dalam penelitian ini berupa Sistem Pendukung Keputusan Seleksi Penerimaan Asisten Praktikum dengan metode Profile Matching. Aplikasi ini digunakan untuk membantu penyeleksi dalam melakukan penilaian kompetensi calon asisten serta memberikan rekomendasi dalam pengambilan keputusan. Kriteria penilaian yang digunakan antara lain tes tulis, tes microteaching, wawancara, dan psikotest. Metode profile matching ini akan membandingkan antara profil peserta dengan profil ideal asisten. Selisih (gap) yang semakin kecil akan membuat kesempatan untuk lolos seleksi semakin besar. Sistem dibangun menggunakan bahasa pemrograman PHP dan MySQL untuk pengolahan data. Pengujian pertama dilakukan dengan membandingkan hasil keputusan peserta yang lolos berdasarkan SPK dengan keputusan dari pengambil keputusan (penyeleksi). Hasil pengujian pertama menunjukkan range tingkat kinerja sistem pada 3 macam studi kasus penerimaan asisten praktikum adalah 60%-86.67%. Pengujian kedua diambil dari hasil User Acceptance Test yang menunjukkan bahwa sebagian besar responden (penyeleksi) bisa menerima aplikasi sistem pendukung keputusan seleksi penerimaan asisten praktikum ini untuk proses penilaian potensi dan …"
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,A Machine Learning Framework for Balancing Training Sets of Sensor Sequential Data Streams,2021,9,Sensors,"Budi Darma Setiawan, Uwe Serdült, Victor Kryssanov",2021/1,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"The recent explosive growth in the number of smart technologies relying on data collected from sensors and processed with machine learning classifiers made the training data imbalance problem more visible than ever before. Class-imbalanced sets used to train models of various events of interest are among the main reasons for a smart technology to work incorrectly or even to completely fail. This paper presents an attempt to resolve the imbalance problem in sensor sequential (time-series) data through training data augmentation. An Unrolled Generative Adversarial Networks (Unrolled GAN)-powered framework is developed and successfully used to balance the training data of smartphone accelerometer and gyroscope sensors in different contexts of road surface monitoring. Experiments with other sensor data from an open data collection are also conducted. It is demonstrated that the proposed approach allows for improving the classification performance in the case of heavily imbalanced data (the F1 score increased from 0.69 to 0.72, p<0.01, in the presented case study). However, the effect is negligible in the case of slightly imbalanced or inadequate training sets. The latter determines the limitations of this study that would be resolved in future work aimed at incorporating mechanisms for assessing the training data quality into the proposed framework and improving its computational efficiency."
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Penentuan portofolio saham optimal menggunakan algoritma genetika,2017,8,Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer,"Rinda Wahyuni, Wayan Firdaus Mahmudy, Budi Darma Setiawan",2017/1/2,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Dalam melakukan investasi di pasar modal investor seringkali dihadapkan pada dua hal yaitu tingkat keuntungan dan tingkat kerugian. Maka dari itu untuk mengurangi tingkat kerugian investor melakukan diversifikasi dengan mengkombinasikan berbagai sekuritas dalam investasi atau disebut dengan portofolio saham. Penelitian ini mengimplementasikan algoritma genetika untuk menentukan proporsi saham agar dapat menghasilkan tingkat keuntungan yang optimal dengan tingkat kerugian yang dapat dipertanggung jawabkan. Berdasarkan hasil pengujian, algoritma genetika mampu menentukan proporsi saham dengan tingkat keuntungan yang lebih besar dan tingkat kerugian yang lebih kecil dari pada perhitungan manual menggunakan single index model. Fitness terbesar 0, 356522 pada kondisi pelatihan algoritma genetika dengan parameter ukuran populasi 100, jumlah generasi 100, crossover rate 0, 3, dan mutation rate 0, 7."
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,"Movie recommender systems using hybrid model based on graphs with co-rated, genre, and closed caption features",2021,7,Register: Jurnal Ilmiah Teknologi Sistem Informasi,"Putra Pandu Adikara, Yuita Arum Sari, Sigit Adinugroho, Budi Darma Setiawan",2021/1/30,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"A movie recommendation is a long-standing challenge. Figuring out the viewer’s interest in movies is still a problem since a huge number of movies are released in no time. In the meantime, people cannot enjoy all available new releases or unseen movies due to their limited time. They also still need to choose which movies to watch when they have spare time. This situation is not good for the movie business too. In order to satisfy people in choosing what movies to watch and to boost movie sales, a system that can recommend suitable movies is required, either unseen in the past or new releases. This paper focuses on the hybrid approach, a combination of content-based and collaborative filtering, using a graph-based model. This hybrid approach is proposed to overcome the drawbacks of combination in the content-based and collaborative filtering. The graph database, Neo4j is used to store the collaborative features, such as movies with its genres, and ratings. Since the movie’s closed caption is rarely considered to be used in a recommendation, the proposed method evaluates the impact of using this syntactic feature. From the early test, the combination of collaborative filtering and content-based using closed caption gives a slightly better result than without closed caption, especially in finding similar movies such as sequel or prequel."
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Analysis of the application of an advanced classifier algorithm to ultra-high resolution unmanned aerial aircraft imagery–a neural network approach,2020,7,International Journal of Remote Sensing,"Fatwa Ramdani, Muhammad Tanzil Furqon, Budi Darma Setiawan, Alfi Nur Rusydi",2020/5/2,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Mapping the existing land use is the essential activity in the management of an area, especially in densely urbanized areas. Knowing the development, amount, and extent of specific land use will be very helpful in management activities. The availability of geospatial data acquisition technology such as unmanned aerial systems (UAS) is currently beneficial for monitoring and inventory activities. Geospatial data with ultra-high resolution are now easily obtained using UAS. This study evaluated the performance of advanced classification algorithms on ultra-high-resolution UAS aerial imagery data based on the different number of regions of interest (ROIs) with two different algorithms, namely, Multilayer Perceptron (MLP) and Radial Basis Function Neural Network (RBFNN). Evaluation was carried out regarding both the performance of computing time and accuracy. The final result shows that the number of ROIs …"
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Extreme Learning Machine Weights Optimization Using Genetic Algorithm In Electrical Load Forecasting,2018,6,Journal of Information Technology and Computer Science,"Vina Meilia, Budi Darma Setiawan, Nurudin Santoso",2018/8/6,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"The growth of electrical consumers in Indonesia continues to increases every year, but it is not matched by the provision of adequate infrastructure that available. This causes the available electrical capacity can't fulfill the demand for electricity. In this study, a smart computing system is build to solves the problem. Electrical load data per hour is being used as an input to do the electrical load forecasting with Extreme Learning Machine method. Extreme Learning Machine method uses random input weight within range-1 to 1. Before the electric load prediction process runs, genetic algorithms first optimizing the input weight. According to the test results with weight optimization, MAPE average error rate is 0.799% while without weight optimization the rate rise to 1.1807%. Thus this study implies that Extreme Learning Machine (ELM) method with weight optimization using Genetics Algorithm (GA) can be used in electrical load forecasting problem and give better prediction result"
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Pengembangan Aplikasi Kamus Istilah Ilmiah Dengan Bahasa Isyarat untuk Peningkatan Kualitas Belajar Siswa Tuna Rungu,2017,6,INDONESIAN JOURNAL OF DISABILITY STUDIES (IJDS),"Budi Darma Setiawan, Fajar Pradana",2017/6/9,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,makna lagu pupus faktor faktor pembentuk tanah murah77 tari jaranan berasal dari keluaran toge hari ini jawaban untuk barakallah pemilik lembu benggolo snapsave download video tiktok savefrom bebas 88 kebaya hijau sage reza lawang sewu video ka matka fast result logo kakek fafafa siapa yang lapaktogel omni slots_app keluaran kamboja hari ini skuad timnas ghana meme_togel 4d cara dapat scatter slot_cars youtube tigermas slot vip judi cara cek turnitin gratis lagu setia selamanya ligaciputra 77 kuat jp rtp accobca slot_games popular slot tunai77 radja manusia biasa chord kpktoto login alternatif jual anakan lovebird jogja ya kafi dewa_togelcom berapa keluar guiyang pools 21 drawing togel web padepokan mimpi bab di wc bangjayy minggu www nagaland lottery result great blue slot_game free download cipit 88a karakter aikatsu tercantik data sydney jayatogel satta ka result aaj ka data sgp 2019 tercepat tim pelaksana kegiatan dana desa saikou ka yo jkt48 amar putusan adalah sholawat jibril nissa sabyan sedanghoki77 senter jarak jauh 10 km slot_gacor_ abis paito sid unguslot cara deposit di angkasa 168 qq slot_pulsa beep sound when put ram to slot nama lain alquran rajawali slot_123 ceki bali tiktok downloader no slot_parkir web download dream high 2 sub indo apakah mosasaurus masih hidup model masjid terbaru pilpres open qualifier result hongkong 2023 result mongolia 6d poki games barbie paitohk 6d paito warna hk 2023 pengeluaran sdy 6d 2021 brtoto wap foto farel prayoga senyum desain bangunan warung sembako lasvegas88 juragan kue semarang lotreonline link hongkong pools warga123 login …
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Mendeteksi Jenis Burung Berdasarkan Pola Suaranya,2016,6,Jurnal Teknologi Informasi dan Ilmu Komputer,"Budi Darma Setiawan, Imam Cholissodin, Rekyan Regasari Mardi Putri",2016/6/20,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Ilmuwan biologi terutama di bidang biodifersitas, terus melakukan penelitian tentang spesies hewan yang ada di dunia. salah satu hewan yang spesiesnya memiliki banyak variasi adalah burung. Tiap jenis burung memiliki perbedaan-perbedaan, mulai dari bentuk anggota tubuhnya, prilakunya, makanannya hingga suaranya. Ilmuwan sering juga mengalami kesulitan untuk melakukan pengamatan di alam. Misalnya, untuk mengetahui spesies burung apa saja yang ada di suatu daerah, mereka harus hadir di suatu wilayah, dan menelusuri setiap pelosok. kadang kala kehadiran mereka di tempat tersebut dalam jangka waktu lama, malah mengusik burung yang ada, dan burung-burung malah pergi meninggalkan tempat, sebelum berhasil diamati. Salah satu cara untuk mendeteksi burung apa saja yang ada di suatu wilayah, tanpa harus mengusik keberadaan burung adalah dengan menggunakan alat bantu. Bisa dengan menggunakan kamera video untuk mengambil gambar lingkungan sekitar, atau dengan perekam suara, untuk merekam suara burung yang ada di sana. Untuk itu penelitian ini ditujukan untuk membuat sebuah pengklasifikasi suara burung secara otomatis. Fitur yang digunakan adalah rhythm, pitch, mean, varian, min, max, dan delta dari suara burungnya. Metode klasifikasi yang digunakan adalah Ekstreme learning Machine (ELM). Dari hasil klasifikasi 4 jenis burung, didapatkan hasil rata-rata akurasi terbaik sebesar 88.82%."
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Comparative Evaluation of Usability between QWERTY-Based Arabic and Non-QWERTY-Based Arabic Keyboard Layout: Empirical Evidence,2020,4,Journal of Information Technology and Computer Science,"Ismiarta Aknuranda, Almira Syawli, Budi Darma Setiawan",2020/7/29,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"QWERTY-based Arabic keyboard layouts have been in existence in order to assist QWERTY users in Arabic typing. However, there was lack of empirical evidence presenting the comparative usability of this layout and the common non-QWERTY-based Arabic keyboard layout. This study examined the usability of a QWERTY-based Arabic keyboard layout (QB) and the common non-QWERTY-based Arabic keyboard layout (NQB) from the perspective of QWERTY users, and compared the evaluation results between the two layouts. After experiments using within-subjects and between-subjects designs, the results showed that QB is significantly better in efficiency and learnability than NQB. QB also enabled more effective typing in almost all experiment designs, except in one between-subjects study. The relatively short interaction time of participants’ first encounter with Arabic keyboards possibly caused this exception. Most participants subjectively preferred QB to NQB in their overall usability."
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Monitoring Road Surface Conditions with Cyclist's Smartphone Sensors,2020,4,CEUR Workshop Proceedings,"Budi Darma Setiawan, Viktor V Kryssanov, Uwe Serdült",2020/3/9,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Road networks form one of the most important infrastructures in modern cities, while road conditions determine the very possibility and quality of land transportation. It is therefore important to monitor and manage road networks properly. The vast area that should be monitored and managed makes this task both expensive and timeconsuming. Recently, an approach to involve road users, such as car drivers, pedestrians, and cyclists, to participate in monitoring road conditions has emerged. Monitoring roads using bicycles has an advantage, compared to using a car, since it allows for reaching narrow roads. This paper presents results of a preliminary study of using a bicycle for detecting road surface defects including potholes, and bumps. Data collected with a cyclist’s smartphone sensors was used to train artificial neural networks in different configurations. The trained networks were then used to detect road surface defects. Results obtained in the experiments indicate that for the accelerometer data, a convolutional neural network provides for the best average accuracy in classifying road surface conditions. Also, this and a long short term memory network produce better results than a standard deep neural network."
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Identification of Patchouli Plants Using Landsat-8 Satellite Imagery And Improved K-Means Method,2017,4,Journal of Environmental Engineering and Sustainable Technology,"Candra Dewi, Muhammad Syaifuddin Zuhri, Achmad Basuki, Budi Darma Setiawan",2017/3/28,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"To maintain the availability of the patchouli plants required monitoring the spread of patchouli plantation. This study performed the identification of patchouli plant through Landsat-8 satellite imagery and Improved K-Means method. Improved was done on this study include the process of determining the initial cluster by specifying the closeness between the data and the determination of the number of cluster (K) by using the histogram equalization technique. The result of internal criteria testing shows that determining the number of clusters using the histogram is less effective because it produces the lower value of the silhouette. On almost all image data test found the best value of the silhouette's coefficient is 75.089% at K= 2 and data in February. Furthermore, based on the results of testing the external criteria known the highest purity value in February data with a number of cluster 5 is 0.6829268. The test results also show that the use of the Improved K-Means on the Landsat-8 image has not been able to recognize the difference patchouli plants with other crops due to the limited resolution of imagery data and also the minimum number and variation of test data. But, visually the patchouli plant cluster is found for February data while the age of the rice crop surrounding the patchouli is still in the early phase of planting."
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Design and Implementation of Earth Image Classification Using Unmanned Aerial Vehicle,2015,4,TELKOMNIKA (Telecommunication Computing Electronics and Control),"Barlian Henryranu Prasetio, Ahmad Afif Supianto, Gembong Edhi Setiawan, Budi Darma Setiawan, Imam Cholissodin, Sabriansyah Rizkiqa Akbar",2015/9/1,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Research in the field of image classification has been widely applied and developed, especially in the field of satellite imagery. Image classification is the process of grouping the pixels in an image into a number of classes, so that each class can describe an entity with certain characteristics. The research aims to build software that can perform the classification of earth image results from UAV (Unmanned Aerial Vehicle) monitoring. The Image converted into YUV format then classified using Fuzzy Support Vector Machine (FSVM). This research designed elements that UAVs will be used for monitoring as follows:(1) the control station, which designed the software on a computer that is used to send or receive data, and display the data in graphical form,(2) payload, using the camera to capture images and send to the control station,(3) communication system using TCP/IP protocol, and (4) UAV, using X650 quadcopter products from xaircraft. All of data can be received if it is sent by several segmented package into smaller parts. The results of image classification, the image of the monitoring carried out on the UAV sized 256 x 256 pixels with a total number of 450 training data size. It is 16x16 pixel image data. Tests performed to classify the image into 3 classes, namely agricultural area, residential area, and water area. The highest accuracy value of 77.69% obtained by the number of training data as much as 375."
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,IMPLEMENTASI ALGORITMA SUBTRACTIVE CLUSTERING UNTUK PEMBANGKITAN ATURAN FUZZY PADA REKOMENDASI PENERIMA BEASISWA,,4,,"W Agung Putra, Lailil Muflikhah, Budi Darma Setiawan",,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Identifying The Influence of Consumer Purchase Intention Through Live Streaming Shopping: A Systematic Literature Review,2024,3,,"Irtiyah Izzaty Mindiasari, Diah Priharsari, Budi Darma Setiawan, Welly Purnomo",2024/4/3,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"The rapid development of technology influences some changes in e-commerce. One of them is the emergence of live-streaming shopping, which combines live-streaming technology with e-commerce, social networking, and entertainment. This shopping format allows viewers to interact with the streamer (seller) and instantly make a purchase with just one touch. Consumers who watch live streaming shopping generally are those who initially have an interest in the offered product. According to prior studies, the presence of live shopping can enhance both customer desire to buy and business sales. To investigate the factors influencing purchase intention in live-streaming shopping, a systematic literature review was conducted. A total of 40 factors were found from 13 selected articles containing live-streaming shopping and purchase intention. Based on these factors, 34 had a positive impact, 2 had a negative impact, and 4 had no significant impact on buyer purchase intention."
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Modified MobileNetV2 with Convolutional Block Attention Module for Facial Expression Recognition,2023,3,,"Muh Hafidh Ilmi Nafi’An, Fitra Abdurrachman Bachtiar, Budi Darma Setiawan",2023/12/1,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Facial emotion recognition is one of the artificial intelligence implementations used to recognize emotions based on data learned by computers. Unlike humans, who can recognize a person’s emotions directly, computers need several trainings procedures conducted by humans to be able to recognize a person’s emotions. Previous research has proposed various methods with deep learning and traditional machine learning approaches to classify emotions based on faces. Some studies obtained relatively high accuracy, but on evaluation by cross-validation, the results were much lower than the accuracy obtained. Therefore, this study proposes an approach using a modified MobileNetV2 deep learning architecture in the residual layer by adding a Convolutional Block Attention Module (CBAM) to improve accuracy and data generalization. This experiment uses the Karolinska Directional Emotional Faces (KDEF …"
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Optimizing Work-From-Anywhere Employee Productivity: A Comprehensive Investigation of the Most Influential Factors Using Factor Analysis and Analytical Hierarchy Process Method,2023,3,,"Neyla Nuril Fauziyah, Diah Priharsari, Budi Darma Setiawan, Aryo Pinandito",2023/10/24,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Working Anywhere or Work From Anywhere (WFA) offers flexibility and convenience for employees to work from anywhere. Workers use communication technologies to stay connected, collaborate with teams, and manage tasks without being limited by geographic or physical space boundaries. However, in practice, applying the WFA model often faces complex challenges. Although technology has made it possible for employees to be virtually connected, some issues have arisen regarding work productivity in a WFA environment. Amid this dynamic, it is crucial to understand what factors contribute to employee work productivity in the WFA model. Several prior studies have conducted an analysis of those factors. However, there are still some gaps in the literature that raise the necessity of monitoring the actual conditions. It is necessary to gain more knowledge of how these factors are related to each other and how …"
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Optimasi Kunjungan Objek Wisata dengan Menggunakan Algoritma Genetik,2016,3,Prosiding Seminar Nasional Teknologi dan Rekayasa Informasi,"Budi Darma Setiawan, Aryo Pinandito",2016,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Rombongan wisata biasanya mengalami kebingungan ketika merancanakan kunjungan wisatanya. Banyaknya pilihan objek wisata yang akan dikunjungi, dan jumlah waktu yang terbatas menyebabkan optimasi perlu dilakukan. Harapannya adalah agar dapat mengunjungi objek wisata sebanyak-banyaknya dan tidak terlalu lama manghabiskan waktu di jalan. Penelitian ini menggunakan algoritma genetic untuk melakukan optimasi. Representasi genetic berupa representasi permutasi untuk menentukan urutan kunjungan. Seleksi elitisme sebesar 30% diterapkan dalam pemilihan individu di tiap generasi. Dari studi kasus yang digunakan dalam percobaan, didapatkan hasil bahwa rata-rata, hasil optimal dapat diperoleh dengan generasi yang lebih sedikit, jika menggunakan cr dan mr masing-masing bernilai 1 dan jumlah individu sebanyak 80."
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Optimasi Vektor Bobot pada Learning Vektor Menggunakan Algoritme Genetika untuk Indentisifikasi Jenis Attention Deficit Hyperactivity Disorder pada Anak,2018,2,Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer,"Raissa Arniantya, Budi Darma Setiawan, Putra Pandu Adikara",2018,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Registrasi Citra Dental Menggunakan Feature From Accelerated Segment Test dan Local Gabor Texture For Iterative Point Correspondence,2017,2,Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK),"Ahmad Afif Supianto, Budi Darma Setiawan",2017/12/31,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"In the periodontal field, research in image registration focused on the evaluation of alveolar bone. One of the processes during the evaluation is the feature extraction stage. Problems caused by an error in the feature extraction stage can arise in the next stage, ie, feature matching. In addition, image registration techniques that are based on features such as pixels, edges detection, contours, or other features are very sensitive techniques for accuracy at the feature extraction stage. From both of these arguments, a robust feature extraction technique is needed to prevent mistakes in the feature matching process to get accurate image registration results. This research proposes a new method in image registration process. The proposed method uses an effective feature extraction method for accuracy by applying Learning Features, which is Feature from Accelerated Segment Test (FAST) and the development of feature matching process by using Local Gabor Texture (LGT) for Iterative Point Correspondence (IPC) algorithm. The experiments were conducted on eight grayscale images, and the results showed that the proposed method successfully registered with an average accuracy value of 93% with a minimum iteration count starting from 400 iterations."
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Pengembangan Multimodal Convolutional Neural Network untuk Grading Buah Jambu Kristal dengan Dua Perspektif Citra,2024,1,Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer,"Yusrian Asghany, Rizal Setya Perdana, Budi Darma Setiawan",2024/5/31,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Indonesia merupakan negara dengan sektor pertanian yang memiliki potensi besar, salah satu produk unggulannya adalah buah-buahan. Terdapat beberapa komoditas buah-buahan di Indonesia yang belum mendapatkan perhatian yang seharusnya, contohnya adalah buah jambu kristal. Upaya peningkatan pemanfaatan jambu kristal dapat dicapai dengan mengoptimalkan proses produksinya. Pengoptimalan ini dapat dicapai dengan menerapkan otomatisasi pada berbagai tahap, dan tahapan grading menjadi salah satu aspek yang sangat menguntungkan. Proses grading dapat diotomatisasi dengan pendekatan computer vision, lebih spesifik Multimodal Convolutional Neural Network (CNN). Pendekatan ini melakukan grading buah jambu kristal dengan masukan citra atas dan citra samping buah. Pendekatan CNN biasa tidak dapat menerima lebih dari satu modalitas sehingga penciri kualitas buah yang diperoleh lebih terbatas dan sangat mungkin untuk tidak mencukupi untuk grading dengan benar. Penelitian dilakukan dengan membangun model Multimodal CNN yang dapat menerima dua macam citra tadi dan menghasilkan prediksi kualitas buah jambu kristal. Model dilatih dengan data pasangan citra atas dan citra samping buah jambu kristal yang sudah melalui pemrosesan awal. Model dengan kinerja terbaik didapatkan dengan penerapan optimizer Adam tanpa scheduler dan learning rate awal sebesar 0.001 pada proses pelatihannya terhadap data yang mendapatkan pemrosesan awal secara lengkap. Model ini mendapatkan nilai akurasi 0.95 dan F1 score 0.95."
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Deteksi Tepi Danau Pada Citra Satelit Menggunakan Metode Canny,2017,1,Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN,"Koko Pradityo, Budi Darma Setiawan, Randy Cahya Wihandika",2017,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Danau adalah sebuah fitur darat yang berperan penting dalam kehidupan manusia. Perubahan pada danau dapat memengaruhi keadaan lingkungan sekitar serta kehidupan masyarakat yang berada disekitarnya. Salah satu cara untuk mengetahui perubahan kondisi danau dilakukan dengan melakukan deteksi tepi permukaan danau pada citra satelit untuk mengetahui perubahan luas danau tersebut. Penerapan algoritme yang tepat dan optimasi lebih lanjut dapat membantu analisis keadaan danau. Aplikasi menerapkan algoritme deteksi tepi Canny untuk melakukan deteksi tepi permukaan danau pada citra satelit. Algoritme segmentasi berdasarkan metode color thresholding diterapkan untuk melakukan optimasi pada performa deteksi tepi. Hasil pengujian menunjukkan bahwa deteksi tepi pada citra satelit dengan hanya menggunakan metode Canny menghasilkan error rate sebesar 57%, sementara proses segmentasi dengan color thresholding meningkatkan kinerja deteksi tepi sebesar 67%."
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,ptimasi Penjadwalan Mata Pelajaran Menggunakan Metode Tabu Search (Studi Kasus: Smkn 2 Singosari),2017,1,Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer (J-PTIIK),"Agus Wahyu Widodo, Budi Darma Setiawan",2017,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Color pixel classification using genetic fuzzy system: Case study on earth surface classification,2010,1,,Budi Darma Setiawan,2010/8/2,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"From satellite image, people can see any objects on the earth surface like houses, streets, lands, vegetation, and water. To classify those objects, observers have to be able to distinguish objects in the image. One of the simplest methods is by analyzing pixel color. In this case, the fuzzy classification system is chosen since there are some overlapping in the pixel color characterized for certain objects. Problem of this system comes when there are no available rules for describing the classification. Therefore, genetic fuzzy system is used for creating the rules. This training process is divided in to two steps which are, learning process to create the group of rules, and tuning process to optimize the fuzzy membership function. The result is measured by CCR (Correct Classification Rate). During the training process, the CCR values are increased after the tuning process is done. The highest CCR value recorded for training …"
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Phenomenological Investigation of Social Media Technological Aspects Against Cyberbullying from the Third Person Perspective of Higher Education Students,2024,0,Journal of Information Technology and Computer Science,"Civica Moehaimin Dhewanty, Diah Priharsari, Budi Darma Setiawan",2024/4/3,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Klasifikasi Ekspresi Wajah Menggunakan Region Selected Facial Landmarks Extraction dan Convolutional Neural Network (CNN),2024,0,Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer,"Cesilia Natasya Nainggolan, Fitra A Bachtiar, Budi Darma Setiawan",2024/3/20,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Klasifikasi Ekspresi Wajah Menggunakan Region Selected Facial Landmarks Extraction dan Convolutional Neural Network (CNN) | Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer j-ptiik logo Beranda Terkini Arsip Tentang Kami Tentang Jurnal Ini Penyerahan Naskah Dewan Editor Kontak Bahasa Indonesia Register Login Klasifikasi Ekspresi Wajah Menggunakan Region Selected Facial Landmarks Extraction dan Convolutional Neural Network (CNN) 1.Beranda / 2.Arsip / 3.Vol 8 No 13 (2024): Publikasi Khusus Tahun 2024 / 4.Artikel Klasifikasi Ekspresi Wajah Menggunakan Region Selected Facial Landmarks Extraction dan Convolutional Neural Network (CNN) Penulis Cesilia Natasya Nainggolan Departemen Teknik Informatika, Fakultas Ilmu Komputer, Universitas Brawijaya Fitra A. Bachtiar Departemen Teknik Informatika, Fakultas Ilmu Komputer, Universitas Brawijaya Budi Darma Setiawan …"
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Prediksi Pergerakan Harga Emas di Tengah Isu Resesi Global 2023 dengan Metode MLP,2024,0,Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer,"Shafira Puspa Fitrotuzzakiyah, Lailil Muflikhah, Budi Darma Setiawan",2024/1/30,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Unit Konseling, Pengembangan Karir, Layanan Terpadu Kekerasan Seksual dan Perundungan Fakultas Ilmu Komputer Universitas Brawijaya (UKPKLTKSP FILKOM UB) melaporkan terdapat sekitar 300 mahasiswa FILKOM yang melakukan konsultasi mengenai kesehatan mentalnya pada tahun 2022-2023. Individu dengan masalah kesehatan mental memiliki motivasi yang rendah dan sering kali sulit untuk terlibat dalam proses memperbaiki kesehatan mentalnya. Penelitian ini bertujuan untuk merancang user experience aplikasi edukasi kesehatan mental berbasis gamifikasi dengan menggunakan pendekatan Player-centered Design (PCD) dan mengukur tingkat efektivitas penggunaan, satisfaction, motivasi pengguna, serta kebergunaan aplikasi melalui evaluasi desain solusi. Pendekatan PCD digunakan dalam melakukan analisis pengguna, termasuk user dan expert interview, kebutuhan pengguna, user …"
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Sistem Genetik Fuzzy untuk Klasifikasi Tutupan Lahan Berdasarkan Foto Udara Unmanned Aerial Vehicle UAV,2023,0,Teknologi Informasi dan Ilmu Komputer,Budi Darma Setiawan,2023/12/30,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,GFS untuk Klasifikasi Tutupan Lahan Berdasarkan Foto Udara UAV,2023,0,Jurnal Teknologi Informasi dan Ilmu Komputer,"Budi Darma Setiawan, Alfi Nur Rusydi",2023/12/30,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Pengamatan terhadap tata letak sebuah wilayah, terutama wilayah berpenduduk, penting dilakukan untuk mengetahui perkembangan dan perubahan yang terjadi. Salah satu pendekatan yang dapat digunakan untuk pengamatan perkembangan suatu wilayah dari waktu ke waktu adalah dengan dengan melihat perubahan tutupan lahan (land cover) secara spasial dengan menggunakan citra foto udara. Foto udara yang mencakup sebuah wilayah dianalisis dengan mengelompokan jenis tutupan lahan atau dikenal dengan land cover classification (klasifikasi tutupan lahan). Metode klasifikasi yang digunakan adalah dengan genetic fuzzy system, yaitu metode klasifikasi dengan menggunakan sistem fuzzy yang aturannya dan fungsi keanggotaannya dioptimasi dengan menggunakan algoritma genetika. Proses metode ini terdiri dari dua tahap yaitu training process, untuk mencari aturan fuzzy yang baik, dan kemudian dilanjutkan dengan tuning process, yaitu proses untuk menggeser batasan nilai pada fungsi keanggotaan himpunan fuzzy yang digunakan. Input program ini adalah nilai red (R), green (G), dan blue (B) dari tiap pixel di dalam citra, dan outputnya adalah kelas pixel yang dikelompokkan (tanah, air, vegetasi, bangunan, dan jalan). Hasil penelitian menunjukkan bahwa nilai fitness tertinggi yang diperoleh adalah hingga 0.84 atau 84%. Abstract Observation of the layout of an area, especially populated areas, is important to monitor what has been changed during the time period. To observe the development of an area from time to time, one approach that can be done is to observe land cover changes from above. Aerial imagery …"
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Genetic Fuzzy System untuk Klasifikasi Tutupan Lahan Berdasarkan Foto Udara Unmanned Aerial Vehicle UAV,2023,0,Jurnal Teknologi Informasi dan Ilmu Komputer,"Budi Darma Setiawan, Alfi Nur Rusydi",2023/12/30,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Factors Influencing Participation in Data Crowdsourcing: A Systematic Literature Review,2023,0,,"Re Miracle Panjaitan, Budi Darma Setiawan",2023/12/8,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Crowdsourcing is becoming a trending concept used by many organizations, without being limited to a particular field. As of 2018, crowdsourcing was used by 85% of the largest worldwide companies. This trend of crowdsourcing would be increasing as it offers numerous advantages to companies in achieving their goals, such as the capability of obtaining data and information from the crowd. The crowd-provided data and information contain the essence of human ingenuity, which enables companies to do problem-solving with a human-centered approach. The more contributors participate, the more likely the crowdsourcing program will succeed. Several studies give different lists of factors that influence the participation rate of the crowd. However, there’s a lack of discussion about the factors and it leads to a situation where organizations need to examine many factors when developing a crowdsourcing platform to …"
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,The Influence of IT Affordances and Emotional Response for Purchase Intention Through Live-Streaming Shopping: Literature Review and Conceptual Model Development,2023,0,,"Irtiyah Izzaty Mindiasari, Budi Darma Setiawan",2023/10/24,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Live-streaming shopping is the development of Web 3.0 technology where the users can interact in real-time and multidimensional without being hindered by distance. Live-streaming shopping is growing rapidly, offering various conveniences through technology that enable customers and the streamer to interact directly, see product information clearly, and reduce uncertainty in online shopping. The ease of utilizing technical features in live streaming shopping is called IT affordances. IT affordances can influence individuals cognitively and affectively through responses. IT affordances consist of visibility, metavoicing, and guidance shopping, which in previous research indirectly indicated an influence on customer emotions through screen bullet and background visual complexity. The consumers' emotions are identified as consumer response, which is the construct affected by IT Affordances. Arousal and pleasure …"
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,GFS Klasifikasi Tingkat Emosi pada Data berbasis Teks menggunakan Multiclass Support Vector Machine,2023,0,Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer,"Ni Luh Made Beathris Anjasari, Fitra Abdurrachman Bachtiar, Budi Darma Setiawan",2023/10/5,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Dampak emosi yang sangat kuat memiliki potensi untuk mempengaruhi fungsi intelektual, keseimbangan hormonal, dan kesehatan mental individu. Studi sebelumnya menunjukkan bahwa gangguan emosional dapat berkontribusi terhadap kondisi seperti depresi dan perilaku yang tidak normal. Depresi, sebagai gangguan kejiwaan yang serius, menyebabkan risiko yang tinggi, termasuk hilangnya minat hidup dan bahkan kecenderungan untuk melakukan tindakan bunuh diri. Oleh karena itu, penting untuk mendeteksi dan mengidentifikasi faktor-faktor pemicu depresi atau stres dengan tujuan memberikan pengobatan yang tepat sesuai dengan pemahaman kondisi emosi seseorang. Penelitian ini bertujuan untuk mengklasifikasikan emosi dengan memanfaatkan metode klasifikasi Support Vector Machine (SVM) guna mengidentifikasi tujuh kelas emosi dalam bahasa Inggris. Sumber data yang digunakan dalam …"
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Mixture of Self-Supervised Learning,2023,0,arXiv e-prints,"Aristo Renaldo Ruslim, Novanto Yudistira, Budi Darma Setiawan",2023/7,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"Self-supervised learning is popular method because of its ability to learn features in images without using its labels and is able to overcome limited labeled datasets used in supervised learning. Self-supervised learning works by using a pretext task which will be trained on the model before being applied to a specific task. There are some examples of pretext tasks used in self-supervised learning in the field of image recognition, namely rotation prediction, solving jigsaw puzzles, and predicting relative positions on image. Previous studies have only used one type of transformation as a pretext task. This raises the question of how it affects if more than one pretext task is used and to use a gating network to combine all pretext tasks. Therefore, we propose the Gated Self-Supervised Learning method to improve image classification which use more than one transformation as pretext task and uses the Mixture of Expert …"
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Fitur Length Of Edge Dan Moment Invarian Untuk Gesture Recognition Dengan Menggunakan Kinect Untuk Kontrol Lampu,2015,0,Jurnal Teknologi Informasi dan Ilmu Komputer,"Rekyan Regasari MP, Budi Darma Setiawan, Issa Arwani",2015/2/17,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"AbstrakTeknologi Kinect adalah teknologi yang dikembangkan untuk game. Kinect memungkinkan pemain game mengontrol permainan dengan menggunakan gerakan dan suara. Hal ini dikarenakan di dalam Kinect terdapat 3 hardware yang bekerja bersama-sama. Tiga hardware tersebut yaitu color VGA video camera, sensor kedalaman, dan multi array microphones. Karena itu, penelitian ini mencoba mengembangkan sensor Kinect untuk keperluan mendeteksi telapak tangan dan gesturnya untuk digunakan sebagai kontrol lampu. Hal ini dilakukan dengan menambahkan beberapa proses pengolahan citra. Pengenalan telapak tangan, menggunakan sensor VGA camera dan depth camera dalam Kinect. Ketika seorang pengguna menjulurkan tangannya kearah sensor, kemudian mengangkat jarinya maka program akan bereaksi. Jika 2 jari yang diangkat maka program akan mengaktifkan saklar padam. Sedangkan jika 5 jari yang diangkat, maka program akan mengaktifkan saklar hidup. Dalam penelitian ini ada 2 fitur yang digunakan dan dibandingkan hasilnya. Fitur yang digunakan adalah fitur Moment Invariant dan Length of Edge. Hasil lebih baik diberikan jika pengenalan dilakukan dengan menggunakan Length of Edge. Dari seluruh data uji yang dipakai, untuk pengenalan dengan menggunakan fitur Length of Edge, akurasi maksimal diperoleh sebesar 100%, sedangkan dengan menggunakan fitur Moment Invariant, akurasi maksimal diperoleh sebesar 80%.Kata kunci: Moment Invarian, Length of Edge, gesture telapak tanganAbstractKinect is a technology developed for game. Kinect allows players to control game play by using …"
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,Implementasi Metode K-Means Clustering Untuk Pembangkitan Aturan Fuzzy Pada Klasifikasi Ketahanan Hidup Penderita Kanker Payudara,2013,0,Repository Jurnal Mahasiswa PTIIK UB,"Khoirul Sholeh, Budi Darma Setiawan, Imam Cholissodin",2013,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,"K-Means clustering merupakan salah satu metode pengelompokkan yang paling sering digunakan diantara algoritma clustering lainnya kerena kesederhanaan dan efisiensinya. Metode ini digunakan untuk mengelompokkan data ketahanan hidup penderita kanker payudara. Data ketahanan hidup penderita kanker payudara yang digunakan yaitu data Haberman’s Survival. Proses clustering data juga bertujuan untuk membangkitkan aturan sebagai rule pengganti yang didefinisikan oleh seorang pakar. Hasil dari pembangkitan aturan, nantinya akan digunakan untuk proses pengujian klasifikasi data dari data ketahanan hidup penderita kanker payudara. Dari proses pengujian bertujuan untuk mengetahui seberapa besar nilai akurasi hasil pengujian dengan menggunakan aturan hasil pembangkitan dengan metode K-Means clustering . Pada penelitian ini secara umum terdapat dua proses utama yaitu proses pelatihan dan proses pengujian. Proses pelatihan sendiri dilakukan dimulai dari proses clustering , perhitungan mean dan standar deviasi, analisa varian, sampai proses ekstraksi aturan fuzzy . Sedangkan untuk proses pengujian dilakukan proses pengujian klasifikasi data menggunakan Fuzzy Inference System Sugeno ordo 1. Pada tahap proses pengujian, dilakukan pengujian data sebanyak 30 data untuk setiap skenario pengujian di masing-masing jumlah cluster . Dari hasil pengujian, dilakukan perhitungan akurasi untuk setiap skenario uji untuk setiap jumlah cluster . Dari seluruh skenario uji, dicari rata-rata akurasi dari seluruh skenario percobaan untuk setiap jumlah cluster . Dari skenario …"
+Budi Darma Setiawan,Pattern Recognition; Behavior Informatics; Smart City,RANCANG BANGUN APLIKASI PEMBACA PARTITUR NOTASI BALOK,,0,,"Ardhian Dharma Yudha Handoyo, Ismiarta Aknuranda, Budi Darma Setiawan",,https://scholar.google.com/citations?hl=id&user=e3kgTUIAAAAJ,

publications/publications_diva_kurnianingtyas.csv

@@ -0,0 +1,50 @@
+Author_Name,Author_Interests,Publication_Title,Year,Citations,Journal,Authors,Publication_Date,Profile_URL,Abstract
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Sistem Basis Data,2017,100,,"Agus Wahyu Widodo, Diva Kurnianingtyas",2017/10/1,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Buku ini membahas konsep-konsep yang mendasari proses perancangan, penggunaan, dan penerapan sistem manajemen basis data. Mulai dari metode, model perancangan, dan bahasa yang digunakan dalam penerapannya."
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Optimasi Derajat Keanggotaan Fuzzy Tsukamoto Menggunakan Algoritma Genetika Untuk Diagnosis Penyakit Sapi Potong,2017,22,Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) p-ISSN,"Diva Kurnianingtyas, Wayan Firdaus Mahmudy, Agus Wahyu Widodo",2017/2/27,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Sistem inferensi fuzzy bisa digunakan untuk diagnosis penyakit pada sapi potong. Untuk mendapatkan akurasi yang tinggi maka batasan fungsi keanggotaan fuzzy perlu ditentukan secara tepat. Penggunaan metode logika fuzzy untuk memperoleh hasil diagnosis penyakit pada sapi potong sesuai pakar berdasarkan batasan gejala penyakit dan aturan-aturan yang diperoleh dari pakar. Batasan tersebut bisa diperbaiki menggunakan Algoritma Genetika untuk mendapatkan akurasi yang lebih baik. Pengujian yang dilakukan pada 51 data dari beberapa gejala penyakit menghasilkan akurasi sebesar 98, 04% dengan menggunakan parameter genetika terbaik antara lain ukuran populasi sebesar 80, ukuran generasi sebesar 15, nilai Crossover rate (Cr) sebesar 0, 9, dan nilai Mutation rate (Mr) sebesar 0, 06. Akurasi tersebut mengalami peningkatan sebesar 3, 54% sesudah dilakukannya optimasi pada metode logika fuzzy."
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Analisis sentimen pemindahan ibu kota Indonesia pada media sosial Twitter menggunakan metode LSTM dan Word2Vec,2023,17,Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer,"Yunico Ardian Pradana, Imam Cholissodin, Diva Kurnianingtyas",2023/8/24,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Pemindahan ibu kota negara dari DKI Jakarta ke Pulau Kalimantan telah menimbulkan perdebatan dan meningkatkan minat publik terhadap isu tersebut. Twitter menjadi media sosial yang populer untuk menyampaikan pendapat dan aspirasi masyarakat. Oleh karena itu, penelitian ini bertujuan untuk menganalisis sentimen masyarakat terkait pemindahan ibu kota menggunakan metode analisis sentimen. Dalam penelitian ini, metode Deep Learning, khususnya Long Short Term Memory (LSTM) dan word2vec yang digunakan untuk menganalisis sentimen tweet masyarakat. Dengan menerapkan metode LSTM dengan Word2Vec, diharapkan dapat diklasifikasikan apakah tweet masyarakat bersifat positif atau negatif terkait pemindahan ibu kota. Model LSTM yang dikembangkan dalam penelitian ini menghasilkan akurasi sebesar 95%, precision sebesar 93%, recall sebesar 93%, dan F1-measure sebesar 95%. Hasil tersebut menunjukkan bahwa metode ini efektif dalam menganalisis sentimen masyarakat terkait pemindahan ibu kota dan dapat memberikan pemahaman yang lebih baik mengenai pandangan publik terhadap perubahan tersebut."
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Sentiment analysis model for klikindomaret android app during pandemic using vader and transformers NLTK library,2022,15,,"Akhmad Ghiffary Budianto, Budisantoso Wirjodirdjo, Iffan Maflahah, Diva Kurnianingtyas",2022/12/7,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"COVID-19 has changed the Indonesian people’s shopping habits for consumer goods. The online retail application came as a response to social distancing and stay-at-home advice. KlikIndomaret is an online retail application that uses the omnichannel concept. As the number of downloads increased, the number of various comments and sentiments on that application also increased. In this study, the researcher did a sentiment analysis aimed to improve the quality of application experiences and retail services. The result of the analysis reflected the services given to customers thus far. The data included reviews and star ratings derived from 4,066 reviews which went under the process of data pre-processing. The methods used in this study were VADER and NLTK, improved by Transformer, without pre-training data. These methods could filter the users’ reviews with sarcasm tone. The results were sentiment …"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,A hybrid symbiotic organisms and variable neighborhood searches to minimize response time,2097,13,AIP Conference Proceedings,"Diva Kurnianingtyas, Muhammad Isnaini Hadiyul Umam, Budi Santosa",2019/4/23,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Medical Emergency Services (MES) are an important element in the modern healthcare system. MES become an important issue because it plays an essential role in saving lives and reducing mortality and disability. The ability of MES is to save lives depending on the time it takes for an ambulance to arrive on the scene after an emergency call received. The purpose of this study is to overcome the problems in the limitation of the number of ambulances required and the minimization of response time. This paper develops the hybrid of Symbiotic Organisms Search and Variable Neighbourhood Search to determine the location and amount of ambulance to be allocated. Our findings showed that for the current system, the ideal limit time is 15 minutes, considering the amount of demand that can be covered. This scenario is able to produce a response time of around 13 minutes and is able to meet the entire demand …"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Synthesis of batik motifs using a diffusion-generative adversarial network,2025,10,Multimedia Tools and Applications,"One Octadion, Novanto Yudistira, Diva Kurnianingtyas",2025/1/22,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"This paper presents a novel approach for generating intricate Batik motifs using a modified Diffusion-Generative Adversarial Network (Diffusion-GAN) augmented with StyleGAN2-Ada. Motivated by the rich cultural heritage of Indonesian Batik, our research addresses the challenge of synthesizing high-quality, diverse patterns that capture the artistry and complexity of traditional designs. Traditional generative models often struggle with stability and fidelity in artistic synthesis. We integrate StyleGAN2-Ada and Diffusion techniques to overcome these limitations, optimizing model architecture and employing a curated Batik dataset. Evaluation metrics including Frechet Inception Distance (FID), Kernel Inception Distance (KID), precision, recall, and non-redundancy assess the quality and diversity of generated motifs. Our results demonstrate significant advancements in the realism and authenticity of synthesized Batik …"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Analisis Judul Majalah Kawanku Menggunakan Clustering K-Means Dengan Konsep Simulasi Big Data Pada Hadoop Multi Node Cluster,2017,9,Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) p-ISSN,"Brillian Aristyo Rahadian, Diva Kurnianingtyas, Dyan Putri Mahardika, Tusty Nadia Maghfira, Imam Cholissodin",2017,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Saat ini pembaca e-magazine seperti majalah Kawanku semakin marak dan terus berkembang. Sehingga penggunaan data besar sangat dibutuhkan pada majalah Kawanku. Selain itu, dibutuhkan pengkategorian setiap bacaan ke dalam tujuh kategori judul pada majalah Kawanku. Sehingga dibutuhkan suatu pengolahan, pengelompokkan, dan pengkomunikasian antar data teks menggunakan text mining. Kombinasi text mining dengan Big Data dapat menjadi sebuah solusi yang menyediakan cara yang efisien dan reliabel untuk penyimpanan data dan infrastruktur yang efektif. Lalu pengkategorian teks dengan clustering K-Means dirasa cukup meskipun menggunakan data besar karena hasilnya memiliki keakuratan yang tinggi. Dari hasil pengujian yang dilakukan, disimpulkan bahwa perbedaan dari banyaknya data tidak mempengaruhi waktu eksekusi karena perbedaan jumlah data yang digunakan tidak terlalu besar."
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Machine Learning,2023,7,,"Lailil Muflikhah, Wayan Firdaus Mahmudy, Diva Kurnianingtyas",2023/12/31,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Machine Learning merupakan cabang dari kecerdasan buatan yang memungkinkan komputer untuk mempelajari pola-pola yang terdapat dalam data agar dapat melakukan prediksi setelah melalui proses pembelajaran. Banyak permasalahan nyata dalam kehidupan sehari-hari dapat diselesaiksan dengan metode pembelajaran, meliputi: prediksi, klasifikasi, rekomendasi, ataupun pengelompokan suatu obyek benda atau kasus tertentu. Buku Ajar Machine Learning ini dimulai dari konsep dasar metode pembelajaran mesin, representasi data, dan pemrosesan awal data. Kemudian dilanjutkan dengan pendekatan secara statistik dalam metode pembelajaran, metode supervised learning, unsupervised learning (clustering), Semi-supervised learning (Reinforcement Learning), serta pengembangan metode pembelajaran mesin (ensemble machine learning). Terakhir, disajikan metode evaluasi pengukuran tingkat performansi kualitas model. Buku ajar ini dilengkapi berbagai contoh soal sehingga mahasiswa dapat mudah memahami dengan belajar secara mandiri dan interaktif. Harapan dari penulis, pembaca bisa memahami dan menganalisis secara tepat dalam pemecahan permasalahan berbasis machine learning serta mengimplementasikan dalam berbagai bidang dalam kehidupan sehari-hari."
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,A system dynamics for financial strategy model assessment in national health insurance system,2020,6,,"Diva Kurnianingtyas, Budi Santosa, Nurhadi Siswanto",2020/4/7,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"The National Health Insurance System (NHIS) was established by the government to provide health insurance to its people. However, some obstacles will be faced by NHIS. For example, when not having proper financial management, the fiscal budget sector will experience a deficit. The issue is happening in Indonesia. The purpose of this research is to develop and evaluate problem models so it can be used for consideration in determining relevant proposed policies. This research uses NHIS data in Indonesia from 2014 to 2018. The method used is a system dynamics approach. The validation of the SD model uses the mean comparison test and t statistic. Next, the model is tested for sensitivity under extreme conditions of low, basic, and high. Patient variables generate low and high states of 34.34% and 33.24%, respectively, which affect the variable fund inventory about 49.3 trillion and -93.46 trillion …"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Structural and Behavioral Validity using a System Dynamic Simulation Approach: The Indonesian National Health Insurance System Problem,2020,5,Proceedings of the 1st International Conference on Industrial Technology - ICONIT,"Diva Kurnianingtyas, Budi Santosa, Nurhadi Siswanto",2020/5/27,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"The dynamic system simulation model (SD) is increasingly favored by researchers in analyzing problems to find policy solutions, particularly in the health sector. The advantage of this approach is that it can predict the system in the long term at the macro level by looking at the interrelationship of behavior between subsystems in the observed system. The purpose of this study is to provide an overview of structural and behavioral validation testing in order to build reliability in the model being built. In this paper, the model developed is the Indonesian National Health Insurance System Problem (INHIS). Here we use structural validation test boundary adequacy and structure verification. Meanwhile, testing the validation of behavior used an average comparison of actual data and data from simulation results. The results obtained are the variables in the INHIS model and are declared valid and accurate because the value of the error ratio obtained (E)< 0.1. The importance of conducting validation has been proven in this study, which produces a valid INHIS model. This causes an increase in the reliability and attractiveness of the INHIS model."
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Financial Strategy Model for Social Health Insurance in Indonesia using Simulation,2019,5,IOP Conference Series: Materials Science and Engineering,"D Kurnianingtyas, B Santosa, N Siswanto",2019/8/1,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Social Health Insurance (SHI) in Indonesia is still facing many obstacles, especially the financial aspects from 2014 to 2017 due to the SHI financial losses that has received more attention from the public and the government in Indonesia. Therefore, this study intends to provide financial strategy recommendations that will get a stability revenue over cost. Dynamic system simulation approaches are used to obtain optimal financial strategies that take into account variable costs and income decisions. The parameter of the load variable is medical costs while the income variable is obtained from the participant premium rate factor. The data come from 2016 and 2017 related data to membership, especially PBPU (Participants Not Wage Recipients). Then, the equation used to find the right strategy is Income≥ Expenditures. In conducting simulations, scenarios are designed to reduce the level of financial losses that occur …"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,System dynamics simulation to determine financial strategy for social health insurance in Indonesia,2019,5,Journal of Physics: Conference Series,"Diva Kurnianingtyas, Budi Santosa, Nurhadi Siswanto",2019/7/1,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Social Health Insurance (SHI) in Indonesia is still experiencing financial constraints because the financial condition of the SHI has continued to be a loss since it was established in 2014 until present so it becomes special attention needed to get achieving the Universal Health Coverage (UHC) target by the government. Therefore, this study intends to provide an appropriate SHI financial strategy recommendation by considering the stability of the balance of income and expense. In addition, a system dynamics simulation approach is needed to find optimal SHI financial strategies with variables including participant premium rates, average cost of benefits, number of health cases, and number of insurance participants. The data used came from BPJS Health data for 2016 and 2017. Afterwards, the equation used was Income≥ Expenditures. In addition, there are several scenarios designed to reduce the level of …"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Sistem Pendukung Keputusan Diagnosis Penyakit Sapi Potong Menggunakan K-Nearest Neighbour (K-NN),2017,5,Jurnal Teknologi Informasi dan Ilmu Komputer,"Diva Kurnianingtyas, Brillian Aristyo Rahardian, Dyan Putri Mahardika, Dwi Angraeni",2017/5/7,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,AbstrakIndustri peternakan merupakan salah satu industri yang penting dalam bidang penyediaan nutrisi makanan sehingga pertumbuhan produk ternak bisa menciptakan suatu ancaman kesehatan masyarakat dimana menyebabkan permasalahan kesehatan. Kurangnya pengetahuan peternak sapi potong mengenai berbagai penyakit yang menyerang serta solusi penanganan salah satu alasan memanajemen kesehatan ternak dirasa cukup menyulitkan beberapa peternak. Pengembangan sistem pendukung keputusan yang menggunakan metode K-Nearest Neighbour (K-NN) sebagai metode inferensi untuk mendiagnosis penyakit ini. Data 11 jenis penyakit dapat dikenali oleh sistem pendukung keputusan dan 20 jenis gejala yang dapat dikenali oleh sistem. Hasil pengujian keakuratan 325 data latih dan 11 data uji telah menghasilkan tingkat akurasi 100% dengan nilai k = 3.Kata kunci: penyakit sapi …
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Optimization of healthcare problem using swarm intelligence: a review,2022,4,,"Agus Wahyu Widodo, Diva Kurnianingtyas, Wayan Firdaus Mahmudy",2022/12/7,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Advances in information technology cause research to focus on involving smart technology to find optimal healthcare. The aim of this study is to provide a brief survey of the latest literature. This study can be used by researchers as basic for development related to healthcare and swarm intelligence (SI). SI optimization algorithm is proven to be able to solve problems from disease diagnosis, scheduling, routing, and service satisfaction. However, the results obtained have not been integrated, accordingly, effective and efficient healthcare services. This study also presents the potential and trends of future healthcare problems that can be solved by the SI."
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Reforming premium amount in the Indonesian National Health Insurance System program using system dynamics,2021,4,Cogent Engineering,"Diva Kurnianingtyas, Budi Santosa, Nurhadi Siswanto",2021/8/10,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"The National Health Insurance System (NHIS) is a government program to help the health needs of its population such as in Indonesia (INHIS). However, there are obstacles to implementing it, which is shown by the results of program evaluations every year that there is a deficit problem, starting in 2014 until 2019 to and even expected to increase in the following years. One of the possible deficits is due to inefficient patient behavior and referral system which will affect the premium amount. The goal is to evaluate and plan further policies with efficient premiums amount. System dynamics is used to simulate the effects of ability-to-pay, willingness-to-pay, salary, age, health cost each customer, and fund inventory. The historical data are used from INHIS from 2014 to 2019, then projected until 2030. The simulation results show that the health costs considered for determining the premiums are critical to the financial …"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Application of Artificial Intelligence for Maternal and Child Disorders in Indonesia: A Review,2024,3,,"Diva Kurnianingtyas, Lailil Muflikhah",2024,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"The development of Artificial Intelligence (AI) technology is used to minimize the risk of maternal disorders during pregnancy. Maternal health needs to be monitored so as not to cause problems during the baby's birth. The purpose of this study is to provide a literature review from 2017 to 2023. The method used is Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). This study is a basis for researchers to help solve maternal health problems using AI. This technology is proven to detect and predict problems during pregnancy, thereby reducing maternal and infant mortality and preventing abnormalities in the development process. In addition, AI can help improve medical personnel's performance by minimizing human error. This study also presents trends in AI problems and methods used. However, the rapid development of AI methods has not provided novelty in solving maternal …"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Solving Deficit Funding Issues in Indonesian Health Insurance Systems,2019,3,,"Diva Kurnianingtyas, Budi Santosa, Nurhadi Siswanto",2019/12/15,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Indonesian National Health Insurance System (INHIS) is a government program that aims to provide health insurance to citizens by guaranteeing their health services. However, INHIS finances continued to experience a deficit since 2014. The purpose of this study was to find a solution so that the stock of funds remained stable. This is done by changing the patient's referral mechanism in INHIS. This is one solution so that the total health care costs do not increase. In this case the dynamic system simulation model is needed to find the relationship pattern between variables so that it can estimate the condition of INHIS funds. The decision variables in this study are the total health care costs and the stock of funds. From the simulation process, the results show that the proposed model can reduce the total health care costs by 44% and be able to maintain a stable supply of funds up to 40% of the sudden increase."
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Comprehensive Review of Meta-Learning Methods for Cold-Start Issue in Recommendation Systems,2025,2,,"Jamallah M Zawia, Maizatul Akmar Binti Ismail, Mohammad Imran, Buce Trias Hanggara, Diva Kurnianingtyas, Silvi Asna, Quang Tran Minh",2025/1/29,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"The cold-start issue in recommendation systems refers to the challenge of recommending items or users when minimal or no prior data is available. Meta-learning methods have emerged as a response to this challenge due to their ability to transfer prior knowledge to recommendation tasks. However, meta-learning techniques are still new, and a general literature review is missing. This paper reviews the existing literature on meta-learning techniques specifically designed to solve the cold-start issue in recommender systems. A systematic review of the literature published between 2018 and June 2024 was conducted, identifying only experimental papers that use meta-learning methods to solve the cold-start issue. Advances, strengths, and weaknesses of such methods were analyzed, and possible research directions for the future were identified. The results demonstrate the application of model-independent meta …"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Comparison Genetics Algorithm and Particle Swarm Optimization in Dietary Recommendations for Maternal Nutritional Fulfillment,2024,2,"SITEKIN: Jurnal Sains, Teknologi dan Industri","Diva Kurnianingtyas, Nathan Daud, Indriati Indriati, Lailil Muflikhah",2024/6,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Fulfilling maternal nutrition is an NP-hard problem. Optimization techniques are required to solve its complexity. This issue is crucial as it affects the number of stunted toddlers in Indonesia. Stunting begins in the womb due to inadequate maternal nutrition during pregnancy. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are optimization methods applied to NP-hard problems, including medicine. Their performance has not been compared in this field. This study aims to identify an alternative method for recommending daily menus based on maternal nutritional needs. There are 55 food ingredients used to fulfill five menu parts: staple food (SF), vegetables (VG), plant source food (PS), animal source food (AS), and complementary (CP). Nutritional adequacy for prenatal is determined by Total Energy Expenditure (TEE) based on basal energy, daily activity, and stress levels. Results show PSO …"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,The necessity of implementing AI for enhancing safety in the Indonesian passenger shipping fleet,2023,2,Kapal: Jurnal Ilmu Pengetahuan dan Teknologi Kelautan,"Shinta JA Rahadi, Dhimas F Prasetya, Muhammad Luqman Hakim, Dian Purnama Sari, Cakra WK Rahadi, Putri Virliani, RD Yulfani, Luthfansyah Mohammad, Diva Kurnianingtyas",2023/12,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Knowledge-enriched domain specific chatbot on low-resource language,2022,2,,"Rizal Setya Perdana, Putra Pandu Adikara, Diva Kurnianingtyas",2022/8/23,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"This study presents architecture in learning human-machine conversation using a human language known as chatbots. Given an utterance, a chatbot attempt to reply using human language by mimicking human intelligence in communication. Previous works in this domain were mostly implemented in English, thus it remains a problem when bringing to a low-resource language, e.g., Indonesian. The limited availability of human-to-human dialog history led to difficulty in the learning process of machine learning algorithms. Therefore, this study proposed a Knowledgeable Chatbot (KC), an enhanced pipeline that enables to receiving of transferred knowledge from another task. A data augmentation pipeline is proposed to handle the limited number of available. To deal with the low-resource language, this study proposed to incorporate a pre-trained language model to gain contextualized language understanding. As …"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,A system dynamic to reforming of the healthcare sector in the Indonesian National Health Insurance System Program,2022,2,International Journal of Industrial and Systems Engineering,"Diva Kurnianingtyas, Budi Santosa, Nurhadi Siswanto",2022,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"National Health Insurance System (NHIS) was established by the Indonesian Government to ensure the health needs of its people. However, the programme encountered many obstacles due to inefficiencies caused by changes in people's behaviour. The aim is to identify key factors, evaluate and plan further policies using Indonesian data from 2014 to 2018. The system's dynamics approach is used to build a model for determining policy alternatives that only focuses on referral reform and limiting health service coverage. The proposed model was proven correct and then implemented in 2019 to plan a policy solution. The result was limiting healthcare coverage as a short-term strategy, whereas changing tiered referrals to combined referrals could be considered a long-term strategy. However, the success of this strategy will only occur if there is good collaboration between health services and regulations. In …"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Cost-Effective Job Sequence Optimization for Minimizing Downtime in Flexographic Printing With Puma Optimizer Algorithm,2024,1,,"Haidar Hanif, Nathan Daud, Diva Kurnianingtyas, Ivan Keane Hutomo, Ivan Gunawan",2024/11/28,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"This paper presents the Puma Optimizer (PO), a recent novel metaheuristic algorithm inspired by the hunting behaviors of pumas. The sequence of the color printing jobs in flexography industry play a significant role since downtime occurs every color changes. Therefore, this study focuses on optimizing printing job sequences to minimize cost, downtime, and maximize revenue. Three experiments of PO algorithm were conducted with varying population sizes and iteration sizes, all resulting in a total revenue of IDR265,434,000 (approximately ${\$}$17,426). Demonstrating the algorithm’s consistency despite different iteration paths. The exploration phase of the PO facilitates diverse solution generation through random searches, while the exploitation phase refines solutions by simulating puma hunting strategies, including ambush and sprinting."
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Analisis Faktor-Faktor yang Memengaruhi Niat Penggunaan Sistem Pembayaran Digital,2024,1,Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi,"Aliya Faradila, Riswan Septriayadi Sianturi, Diva Kurnianingtyas",2024/8/15,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Based on observations and interviews with passengers of Trans Jatim Corridor I Bus, found that most of them still pay tickets in cash. Digital payment system considered less efficient, because conductors have to walk around and difficulty using phones when the bus is crowded, making cash payments more preferable. This study aims to analyze the factors influencing the use of the digital payment system in the Trans Jatim Corridor I bus based on the UTAUT model and using SEM-PLS analysis techniques. The results show that the intention to use the system is influenced by facilitating conditions, while performance expectancy, effort expectancy, and social influence variables have no effect. Gender, age, and experience do not moderate the relationship between these variables and behavioral intention to use the system. It can be concluded that if Trans Jatim Corridor I bus passengers have adequate facilities to use digital payments, they will have a high intention to use the system."
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Application of Data Augmentation on SSD Mobilenet for Detection of Kenaf Plant Disease and Pest,2023,1,,"Agus Wahyu Widodo, Alfita Rakhmandasari, Wayan Firdaus Mahmudy, Muh Arif Rahman, Diva Kurnianingtyas",2023/10/24,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"The Kenaf plant is a source of natural fibre and can potentially be a source of bioenergy, biopharmaceuticals, bioremediation, and others. The need for Kenaf plant fibre is increasing due to the variety of plant uses. This high demand is different from a high production level as well. One of the main production barriers is pests and diseases in plants. Rapid detection of pests and diseases is needed to carry out appropriate actions. Pest and disease detection can be assisted with deep learning methods such as Single-Shot Multibox Detection (SSD) Mobilenet. Data augmentation is carried out to improve the accuracy of the results. Although the Mobilenet SSD gets an accuracy of only 54%, using data augmentation can increase the accuracy of the Mobilenet SSD by 14.02% to 72.02%. This result is better than using Faster Region-based Convolutional Neural Network (RCNN), which is used as a comparison method."
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Opportunities of Artificial Intelligence for Authentication and Assurance of Halal Products,2025,0,Journal of Information Technology and Computer Science,"Wayan Mahmudy, Diva Kurnianingtyas",2025/5/2,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"The need for Muslims to obtain halal food encourages the development of systems that can assist in the authentication and assurance of halal products. Various Artificial Intelligence (AI) based systems have been developed to meet these needs. AI is a technology that enables computers to understand, learn, and perform tasks that would typically require human intelligence and reasoning to act and make decisions. With the help of high-speed computers, AI algorithms learn patterns in available data and perform assigned tasks with high accuracy and efficiency. This study focusses on the application of AI for the identification, monitoring, verification and validation of halal products. Also, this paper limits the discussion to food products with the topics of (1) Inspection of raw materials and detection of contamination to ensure the halalness of raw materials (2) Market monitoring and recommendation of halal restaurants to help Muslim consumers. The consumers were looking for a restaurant that suits their needs and preferences (3) Verification of halal certification to ensure that the certification given to the product comes from a competent and trustworthy authority."
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Performance Evaluation of Different Classification Algorithms Applied for Identifying Maternal Nutritional Status by Anthropometric Measurements,2025,0,International Journal of Integrated Engineering,"Diva Kurnianingtyas, Nathan Daud, Agus Wahyu Widodo, Tutut Herawan",2025/4/30,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Pregnancy significantly influences infant quality and development. Maternal monitoring, indicated by body mass index (BMI) and mid-upper arm circumference (MUAC) measurements, reflects a country's socioeconomic development. Improper measurements heighten the risk of chronic energy deficiency (CED) in pregnant women and low birth weight (LBW) in infants. This study leverages artificial intelligence (AI) to enhance the detection process. Specifically, it evaluates the prediction performance of various classification methods: Decision Tree (DT), K-Nearest Neighbors (KNN), Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM). Using interviews in Jombang District, Indonesia, these methods were expected to identify maternal nutritional status. The model design was divided into two stages: MUAC estimation generated binary classes, and BMI estimation generated multiple classes. The evaluation of these methods included various performance metrics: Accuracy (Acc), G-means, Sensitivity (Sens), Specificity (Spec), Positive Predictive Value (PPV), and Negative Predictive Value (NPV). Based on the results, all methods are proposed for both classifications, except KNN on multiple classification. KNN achieved significant scores in all matrices with p<0.01. KNN's performance is impacted by data imbalance. The study revealed a strong correlation (0.92 coefficient) between BMI and MUAC variables. The application of ML algorithms in detecting maternal nutritional status can significantly enhance the effectiveness and efficiency of health facilities, especially in areas with inadequate resources …"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Chest X-Ray Images Clustering Using Convolutional Autoencoder for Lung Disease Detection,2025,0,JOIV: International Journal on Informatics Visualization,"Putri Amanda Syafira, Novanto Yudistira, Diva Kurnianingtyas",2025/3/31,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"In healthcare, medical imaging is commonly used for health assessments. One of the most commonly used types of medical imaging is X-ray imaging. One area that often undergoes examination using this modality is the lungs, where healthcare professionals use X-ray images to interpret the results. However, prolonged interpretation of X-ray results by healthcare professionals and other work activities can lead to errors and potentially result in invalid disease identification. There is a need for a system that can classify the detection results from these images to assist healthcare professionals in their tasks. Various methods can be used for this purpose, such as classification, clustering, segmentation, etc. However, data labeling requires significant resources and costs, especially with large-scale datasets. One possible solution is to use an unsupervised learning approach to address this. One method under unsupervised learning is clustering, which allows the system to process and understand data patterns without needing external annotations or manual labeling. This research uses an autoencoder as a subcategory of unsupervised learning. This is because autoencoders can automatically extract relevant features from the data without needing external label guidance. The research utilizes a dataset consisting of 700 X-ray images of the chest, including 500 images showing disease and 200 normal X-ray images. This research aims to determine the effectiveness of clustering methods using an autoencoder model in grouping X-ray image results. The research conducted two experiments. In the first experiment, an autoencoder with 18 Layers was …"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Deteksi Tingkat Resiko Kanker Serviks pada Wanita Usia Subur dengan Metode Modified K-Nearest Neighbor,2025,0,Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer,"Dillah Lyra Mukhrodi, Lailil Muflikhah, Diva Kurnianingtyas",2025/2/6,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Kanker serviks merupakan kanker kedua yang paling banyak diderita oleh wanita di Indonesia. Penyebab utama kanker serviks 99, 7% berkaitan erat dengan infeksi virus Human Papilloma. Salah satu metode yang digunakan untuk skrining kanker serviks adalah IVA (Inspeksi Visual dengan Asam Asetat), yang berfungsi mendeteksi keberadaan sel-sel abnormal sejak dini. Namun, metode IVA memiliki kekurangan, yakni tingkat sensitivitas dan spesifitas yang rendah. Maka, penelitian ini dilakukan dengan tujuan untuk mendeteksi tingkat resiko kanker serviks dengen menerapkan metode Modified K-Nearest Neighbor (MKNN). Metode tersebut dipilih karena mampu menangani data outlier dan tidak seimbang. Metode ini bekerja dengan menghitung jarak antar data sambil menyesuaikan bobot tetangga berdasarkan jaraknya sehingga menghasilkan klasifikasi yang lebih representatif. Dataset penelitian mencakup 314 pasien kanker serviks. Setelah melalui tahapan data preprocessing, pembagian"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Eksplorasi Skema Reproduksi Algoritma Genetika untuk Vehicle Routing Problem dengan Time Windows (Studi Kasus: Anekapay),2025,0,Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer,"Haidar Hanif, Diva Kurnianingtyas, Agus Wahyu Widodo",2025/1/15,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,Eksplorasi Skema Reproduksi Algoritma Genetika untuk Vehicle Routing Problem dengan Time Windows (Studi Kasus: Anekapay) | Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer j-ptiik logo Beranda Terkini Arsip Tentang Kami Tentang Jurnal Ini Penyerahan Naskah Dewan Editor Kontak Bahasa Indonesia Register Login Eksplorasi Skema Reproduksi Algoritma Genetika untuk Vehicle Routing Problem dengan Time Windows (Studi Kasus: Anekapay) 1.Beranda / 2.Arsip / 3.Vol 9 No 13 (2025): Publikasi Khusus Tahun 2025 / 4.Artikel Eksplorasi Skema Reproduksi Algoritma Genetika untuk Vehicle Routing Problem dengan Time Windows (Studi Kasus: Anekapay) Penulis Haidar Hanif Universitas Brawijaya Diva Kurnianingtyas Universitas Brawijaya Agus Wahyu Widodo Universitas Brawijaya Abstrak Naskah ini akan diterbitkan di Engineering Optimization Journal Diterbitkan 15 Jan 2025 Cara …
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Penjadwalan Makan Otomatis untuk Ibu Hamil Menggunakan Algoritma Genetika pada Aplikasi Mobile Berbasis Jetpack Compose,2025,0,Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer,"I Gusti Ngurah Mayun Suryatama Giri, Diva Kurnianingtyas, Fais Al Huda",2025/1/14,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Penjadwalan Makan Otomatis untuk Ibu Hamil Menggunakan Algoritma Genetika pada Aplikasi Mobile Berbasis Jetpack Compose | Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer j-ptiik logo Beranda Terkini Arsip Tentang Kami Tentang Jurnal Ini Penyerahan Naskah Dewan Editor Kontak Bahasa Indonesia Register Login Penjadwalan Makan Otomatis untuk Ibu Hamil Menggunakan Algoritma Genetika pada Aplikasi Mobile Berbasis Jetpack Compose 1.Beranda / 2.Arsip / 3.Vol 9 No 13 (2025): Publikasi Khusus Tahun 2025 / 4.Artikel Penjadwalan Makan Otomatis untuk Ibu Hamil Menggunakan Algoritma Genetika pada Aplikasi Mobile Berbasis Jetpack Compose Penulis I Gusti Ngurah Mayun Suryatama Giri Putra Universitas Brawijaya Diva Fais Kata Kunci: Algoritma Genetika, jadwal makan otomatis, Kekurangan Energi Kronis, Jetpack Compose. Abstrak Naskah ini akan diterbitkan di e-…"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Implementasi Sistem Deteksi Anomali pada Jaringan Komputer dengan Pendekatan XGBoost dan Data SNMP,2025,0,Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer,"Amadeo Muhammad Augie Rudianto, Eko Sakti Pramukantoro, Diva Kurnianingtyas",2025/1/10,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Reliabilitas jaringan sangat bergantung pada kemampuan sistem untuk mendeteksi dan merespons anomali secepat mungkin. Anomali dalam jaringan dapat berupa aktivitas mencurigakan, kegagalan perangkat keras, atau serangan siber yang mengancam ketersediaan layanan. Penelitian ini bertujuan untuk mengembangkan sistem deteksi anomali jaringan berbasis data Simple Network Management Protocol (SNMP) menggunakan model XGBoost. Data SNMP yang digunakan pada penelitian ini mencakup berbagai metrik jaringan, sehingga memungkinkan sistem untuk mendeteksi pola-pola kompleks secara akurat. Penelitian ini mencakup proses pengembangan model klasifikasi, implementasi sistem inferensi deteksi anomali berbasis SNMP, dan evaluasi kinerja sistem melalui skenario pengujian yang realistis. Hasil pengujian menunjukkan bahwa model XGBoost mampu mendeteksi berbagai jenis serangan dengan akurasi mencapai 99, 82% pada data uji yang belum pernah dilihat. Sistem inferensi yang diimplementasikan juga mampu bekerja secara kontinu untuk memproses data SNMP dan memberikan hasil prediksi yang konsisten. Namun, penelitian ini juga menemukan bahwa sistem memiliki keterbatasan dalam hal fleksibilitas untuk menangani pola serangan baru yang tidak terwakili dalam data pelatihan. Penelitian ini berhasil mengembangkan dan mengimplementasikan sistem deteksi anomali berbasis SNMP dengan model XGBoost yang andal dan akurat. Temuan ini diharapkan dapat menjadi landasan bagi pengembangan lebih lanjut untuk meningkatkan fleksibilitas dan adaptabilitas sistem dalam lingkungan …"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Rekomendasi Menu dengan Bahan Makanan Alternatif Berdasarkan Kategorisasi Nutrisi menggunakan K-Means dan BERT,2025,0,Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer,"Muhammad Jilan Naufal, Diva Kurnianingtyas",2025/1/8,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Rekomendasi Menu dengan Bahan Makanan Alternatif Berdasarkan Kategorisasi Nutrisi menggunakan K-Means dan BERT | Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer j-ptiik logo Beranda Terkini Arsip Tentang Kami Tentang Jurnal Ini Penyerahan Naskah Dewan Editor Kontak Bahasa Indonesia Register Login Rekomendasi Menu dengan Bahan Makanan Alternatif Berdasarkan Kategorisasi Nutrisi menggunakan K-Means dan BERT 1.Beranda / 2.Arsip / 3.Vol 9 No 13 (2025): Publikasi Khusus Tahun 2025 / 4.Artikel Rekomendasi Menu dengan Bahan Makanan Alternatif Berdasarkan Kategorisasi Nutrisi menggunakan K-Means dan BERT Penulis Muhammad Jilan Naufal Universitas Brawijaya Diva Kurnianingtyas Universitas Brawijaya Indriati Universitas Brawijaya Kata Kunci: BERT, K-Means, Kategori Nutrisi, Rekomendasi Menu Makanan Abstrak Naskah ini akan diterbitkan di jurnal …"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,"Digital Based Branding of Tourism and MSME Product in Tasikmadu and Gemaharjo Village, Trenggalek Regency",2024,0,Journal of Innovation and Applied Technology,"Candra Dewi, Bayu Rahayudi, Wayan Firdaus Mahmudy, Diva Kurnianingtyas",2024/12/19,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Tasikmadu and Gemaharjo villages are two villages in Watulimo have considerable potential in tourism and MSME products. However, rural communities including tourism actors, village officials, and MSME players are still unable to maximize the potential of the village, due to the lack of ability of tourism managers and MSME actors in promoting tourism and its products widely. Therefore, this activity intends to increase the knowledge and ability of tourism actors, village officials, and MSME actors from Tasikmadu and Gemaharjo villages in promoting tourism and MSME products. This is done by developing tourism profiles and MSMEs digitally. The evaluation results showed that the activity was quite successful in increasing tourism promotion in Tasikmadu by utilizing the village's Instagram social media. In addition, the success of MSME promotion can also be seen by making logos and packaging for MSME products, both in Tasikmadu and Gemaharjo."
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,A review of machine learning methods to build predictive models for male reproductive health,2024,0,,"Ariawan Adimoelja, Wayan Firdaus Mahmudy, Diva Kurnianingtyas",2024/12/4,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,A Deep Learning Approach to Plastic Bottle Waste Detection on the Water Surface using YOLOv6 and YOLOv7,2024,0,"Engineering, Technology & Applied Science Research","Naufal Laksana Kirana, Diva Kurnianingtyas",2024/12/2,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Deep learning is a branch of machine learning with many layers, such as the You Only Look Once (YOLO) method. From various versions of YOLO, YOLOv6 and YOLOv7 are considered more prominent because they achieve high Mean Average Precision (mAP) values. Both versions of YOLO have been implemented into various problems, especially in the waste detection problem. Plastic bottle waste is one of the most common types of waste that pollutes Indonesian waters. This study aims to solve this problem by helping to sort waste in surface waters by applying YOLOv6 and YOLOv7. FloW-Img was used, obtained on request from the Orcaboat website. The dataset consists of 500,000 bottle objects in 2,000 images. The YOLOv6 and YOLOv7 models were evaluated using mAP and running time. The results show that YOLOv6 and YOLOv7 can handle bottle waste detection well, with mAP values of 0.873 and 0.512, respectively. In addition, YOLOv6 (4.21 m/s) has a higher detection speed than YOLOv7 (13.7 m/s). However, in tests with images that do not have bottle objects, YOLOv7 provides better detection accuracy and consistency results, making it more suitable for real-world applications that demand high accuracy in environments with much visual noise."
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Food Image Classification for Maternal Nutritional Fulfillment Using MobileNet,2024,0,,"Nathan Daud, Diva Kurnianingtyas",2024/11/28,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Proper nutrition during pregnancy is crucial for the health of both the mother and the fetus. However, many pregnant women struggle to maintain an adequate diet due to a lack of accessible dietary monitoring tools. This study aims to enhance dietary monitoring for pregnant women by comparing the performance of traditional Convolutional Neural Networks (CNN) and the pre-trained MobileNet V2 model in food image classification. Given the increasing reliance on mobile health applications, especially in regions like Indonesia, it is essential to develop efficient and accurate food recognition systems that can operate on mobile devices. Our research involves training and evaluating both CNN and MobileNet V2 models on a dataset of food images commonly consumed by pregnant women. The models' performance is assessed based on accuracy, precision, recall, and inference time. The results show that MobileNet V2 …"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Leveraging AI Models for Enhanced Urine Analysis: Evaluating YOLOv8 and MobileNet-v2 in Dehydration Detection Post-COVID-19,2024,0,,"Nathan Daud, Alexander Imanuel Widjanarko, Diva Kurnianingtyas",2024/11/28,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"The COVID-19 pandemic has underscored the critical importance of effective health management, particularly in monitoring hydration levels due to the virus’s extensive multisystem impacts. This study delves into the potential of advanced artificial intelligence (AI) models, specifically YOLOv8 and MobileNet-v2, in accurately analyzing urine color and levels as indicators of dehydration. By utilizing a dataset from Roboflow Universe, comprehensive data augmentation techniques were employed to enhance model training, ensuring robustness across various lighting conditions and diverse realworld scenarios. Both YOLOv8 and MobileNet-v2 models achieved perfect scores of for accuracy, recall, precision, and F1-score in the urine sample classification task. However, YOLOv8 demonstrated superior performance in real-time detection, positioning it as the more suitable candidate for clinical applications. The …"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Face Expression Recognition on Ojek Online for Safety Improvement Using Deep Learning,2024,0,,"Nathan Daud, Alexander Imanuel Widjanarko, Naomi Sitanggang, Diva Kurnianingtyas",2024/11/22,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"During COVID-19, many workers were laid off because the company was struggling with financial challenges. Hence, laid-off workers have switched professions to become Ojek Online (Ojol) drivers because they have the flexibility to work with adequate income. In addition, the COVID-19 period has changed people’s behavior to choose Ojol because it is easy and practical. The growing number of demands has the risk of accidents due to fatigue and lack of adequate rest time. This study aims to implement deep learning methods such as Convolutional Neural Network (CNN), Bidirectional Long-Short Term Memory (BiLSTM), and Bidirectional Gated Recurrent Unit (BiGRU) to minimize the accident risk with face expression recognition. It will also compare the three methods to find the best method. About 350 primary data were collected from the participants’ questionnaires, grouped into seven facial expression …"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Improving Green Practice Readiness in Indonesian Beef Value Chain,2024,0,Industrial Engineering & Management Systems,"Hana Catur Wahyuni, Inggit Marodiyah, Marimin Marimin, Ivan Gunawan, Diva Kurnianingtyas",2024/9,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Due to the gap between supply and demand, Indonesia has not been able to meet the domestic demand for beef without imports. With the rapid increase in population, the gap continues to widen, making self-sufficiency difficult to attain. With the rising environmental awareness, the green value chain is considered a viable solution, which will promote beef self-sufficiency while improving environmental performance. This study develops a PESTLE-based actor preparedness questionnaire to explore the Indonesian beef value chain readiness in implementing green practices. The questionnaire considers political, economic, social, technological, legal, and environmental dimensions. The survey involved 37 actors in the Indonesian beef value chain. The survey’s results were analyzed using multidimensional scaling. The results show that cattle breeders were the least prepared to implement green practices, and …"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Perbandingan Kinerja Model YOLOv6 dan YOLOv7 dalam Mendeteksi Sampah Perairan,2024,0,Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer,"Naufal Laksana Kirana, Diva Kurnianingtyas",2024/7/10,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Once (YOLO) yang merupakan metode yang digunakan global. YOLOv6 dan YOLOv7 menonjol dengan nilai mean average precision (mAP) tinggi. Penerapan deep learning dapat dilakukan untuk deteksi sampah perairan mengingat perairan Indonesia lebih besar dibanding daratan. Sampah plastik merupakan salah satu jenis sampah terbesar yang mencemari Indonesia. Penelitian diawali dengan studi literatur untuk penggalian informasi, dilanjutkan dengan mengumpulkan data, dilanjutkan dengan merancang model YOLOv6 dan YOLOv7. Setelah model dibuat, dilanjutkan dengan pelatihan model dan dilanjutkan dengan evaluasi dan analisis kinerja model. Penelitian diakhiri dengan penarikan kesimpulan terkait perbandingan model. Data yang digunakan pada penelitian ini yakni FloW-IMG yang didapat dengan cara request pada website orcaboat. Kedua model yang telah dirancang berhasil …"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Comparison Genetics Algorithm and Particle Swarm Optimization in Dietary Recommendations for Maternal Nutritional Fulfillment,2024,0,"SITEKIN: Jurnal Sains, Teknologi dan Industri","Diva Kurnianingtyas, Nathan Daud, Indriati Indriati, Lailil Muflikhah",2024/6,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,FMEA for Blockchain Design for Food Safety and Halal Risk Mitigation in Meat Supply Chain,2024,0,International Journal of Industrial Engineering,"Hana Catur Wahyuni, Rahmania Sri Untari, Rima Azzara, Diva Kurnianingtyas, Marco Tieman",2024,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"This research discusses the application of the Failure Mode and Effect Analysis (FMEA) method in designing a blockchain system for mitigating food safety and halal risks in the beef supply chain. The complexity of the meat supply chain involving various parties increasing the risk of contamination and changes in the halal status of the meat. This research aims to identify food safety and halal risks, prioritise the risks, and design blockchain-based mitigation solutions. Blockchain was chosen for its advantages in providing high transparency and accountability, enabling real-time tracking at every stage of the supply chain. The research results show that most of the risks in the meat supply chain fall into the low category, but there are some critical medium risks, especially related to the slaughtering process. The proposed blockchain design includes product traceability features, halal certification, temperature monitoring, and smart contracts to ensure automatic validation of food safety and halal compliance. The implementation of this blockchain is expected to increase consumer trust in meat products, reduce the risk of contamination, and strengthen accountability throughout the meat supply chain."
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,FMEA for Blockchain Design for Food Safety and Halal Risk Mitigation in Meat Supply Chain,2024,0,,"Untari Rahmania Sri, Rima Azzara, Marco Tieman, Diva Kurnianingtyas",2024/1/1,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"This research discusses the application of the Failure Mode and Effect Analysis (FMEA) method in designing a blockchain system for mitigating food safety and halal risks in the beef supply chain. The complexity of the meat supply chain involving various parties increasing the risk of contamination and changes in the halal status of the meat. This research aims to identify food safety and halal risks, prioritise the risks, and design blockchain-based mitigation solutions. blockchain was chosen for its advantages in providing high transparency and accountability, enabling real-time tracking at every stage of the supply chain. The research results show that most of the risks in the meat supply chain fall into the low category, but there are some critical medium risks, especially related to the slaughtering process. The proposed blockchain design includes product traceability features, halal certification, temperature monitoring, and smart contracts to ensure automatic validation of food safety and halal compliance. The implementation of this blockchain is expected to increase consumer trust in meat products, reduce the risk of contamination, and strengthen accountability throughout the meat supply chain."
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Optimizing Sensitive Weight Configurations on a Fast-Planing Vessel to Reduce Drag,2024,0,,"Muhammad Luqman Hakim, Dian Purnamasari, Luthfansyah Mohammad, Patricia Evericho Mountaines, Diva Kurnianingtyas, Dendy Satrio, Muhammad Hafiz Nurwahyu Aliffrananda",2024,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"In the realm of high-speed planing crafts, characterized by their diminutive size, an acute sensitivity to variations in weight and center of gravity is observed. This investigation delves into the consequential influence of alterations in weight and center of gravity on the resistance encountered by these crafts. The primary goal is to scrutinize a methodology aimed at optimizing weight and center of gravity for small, high-speed planing crafts, with the explicit aim of minimizing drag and amplifying overall performance. It is revealed that fluctuations in weight and center of gravity exert a substantial impact on the craft's resistance. The study adopts an integrated approach, incorporating Design of Experiment (DOE), Response Surface Method (RSM), and Computational Fluid Dynamics (CFD). Systematic adjustments to weight and center of gravity induce discernible effects on sinkage and hull trim, yielding distinctive resistance values. Empirical findings highlight that a marginal backward shift in the center of gravity, constituting approximately 1% of the vessel's length, culminates in a nearly 5% reduction in drag. However, immoderate backward or forward shifts precipitate an undesirable increase in resistance. This research underscores the versatility of the combined methodology in optimizing the center of gravity across diverse hulls or scenarios, with broader implications for enhancing the performance of high-speed planing crafts across varied contexts."
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Strategi Rujukan Dan Premi Pada Sistem Asuransi Kesehatan Nasional,2021,0,,Diva Kurnianingtyas,2021/8/16,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Sistem Asuransi Kesehatan Nasional (SAKN) merupakan program pemerintah untuk membantu kebutuhan kesehatan penduduknya. Indonesia merupakan salah satu negara pelaksana program SAKN. Namun memiliki kendala dalam pelaksanaannya, yang ditunjukkan dari hasil evaluasi program setiap tahun bahwa terdapat masalah defisit mulai tahun 2014 sebesar Rp 3, 3 triliun dan meningkat tajam pada tahun 2019 menjadi Rp 50, 9 triliun, bahkan diperkirakan akan meningkat dalam beberapa tahun mendatang. Salah satu kemungkinan faktor penyebab defisit adalah ketidakefisienan pada perubahan dari perilaku pasien dan sistem rujukan yang berdampak pada besaran premi. Tujuan penelitian ini yaitu mengidentifikasi faktor-faktor utama, mengevaluasi, dan merencanakan kebijakan selanjutnya dengan data historis mulai tahun 2014 hingga 2019. Pendekatan sistem dinamik digunakan untuk membangun model dan mensimulasikan pengaruh kepesertaan, perawatan kesehatan, pendapatan premi, dan biaya layanan. Hasil simulasi kondisi eksisting menunjukkan bahwa ketika peserta tidak menunggak pembayaran premi setiap bulannya maka rata-rata pendapatan premi dari 2014 hingga 2019 yang seharusnya diperoleh Rp80, 5 triliun tetapi kenyataannya hanya sebesar Rp73 triliun yang diterima. Selain itu, berdasarkan uji sensitivitas, pengembangan model akan difokuskan pada mekanisme rujukan yang memiliki pengaruh besar pada biaya layanan dan premi yang mempengaruhi pendapatan premi. Ada dua model usulan yan dikembangkan menjadi tiga model perencanaan antara lain (1) model rujukan berjenjang diubah …"
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,Decentralization of Medical Emergency Service to Minimize Response Time,2019,0,"Industrial Engineering and Operations Management, ISSN: 2169-8767","Muhammad Isnaini Hadiyul Umam, Diva Kurnianingtyas, Budi Santosa, Nurhadi Siswanto",2019,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Medical Emergency Service (MES) is an important element in modern healthcare system. MES becomes important issue because it plays an important role in saving lives and reducing mortality and mordibility. The ability of MES to save lives depends on the time it takes for an ambulance to arrive on the scene after an emergency call received. This research will focus on changing the MES system from initially centralized to decentralized by considering the determination of the allocation and redeployment of ambulance. We propose the Nearest Neighbourhood–Symbiotic Organisms Search algorithm (NN-SOS) to overcome the problems. This study is expected to be able to solve the problems in the limitation of the number of ambulance required and the minimization of response time. From this study, it can be concluded that a decentralized ambulance system is needed. The comparison of the response time generated from the two systems is a centralized system with the best time limit having an average response time of 10-13 minutes while the decentralized system is better which is 3-6 minutes."
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,An Examination of Herding Behavior Among Tech Stock Investors in Asean-6 Countries: A Bilstm Machine Learning Approach,,0,Available at SSRN 5142816,"Hussain Rammal, Putu Gayatri, Asfi Manzilati, Silvi Asna Prestianawati, Diva Kurnianingtyas, Nathan Daud, Abdurrahman Hakim, Muhammad Fawwaz",,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"Southeast Asia, as an emerging region, is experiencing a rapid digital transformation fueled by the exponential growth of the digital economy, fast tech advancement, and widespread adoption of AI solutions. This fosters a thriving tech startup ecosystem, attracting significant investor interest in tech stocks, particularly as the focus shifts towards profitable growth in ASEAN-6 countries. However, the enthusiasm may increase the potential of herding behavior among investors. Hence, this research investigates the potential presence of irrational behavior of investors, and herding behavior, in tech stocks of these ASEAN-6 countries from January 2021 to April 2024. This study employs the Cross-Sectional Absolute Deviation (CSAD) to identify non-linearity in market returns, a potential indicator of herding. Additionally, it leverages Bi-Directional Long Short-Term Memory (BiLSTM), a powerful machine learning tool to make accurate stock predictions and capture data trends. Therefore, by understanding whether investors are following the crowd or making informed decisions, this study aims to contribute to a deeper understanding of investor behavior in this dynamic and evolving market. The findings can offer valuable insights for investors, policymakers, and tech companies on navigating the rapidly changing technological landscape in ASEAN-6 countries."
+Diva Kurnianingtyas,Optimization; Soft Computing; Industrial Informatics; Industrial Artificial Intelligence,REVISI PROPOSAL PROGRAM PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT PENELITIAN PENDIDIKAN MAGISTER MENUJU DOKTOR UNTUK SARJANA UNGGUL TAHUN ANGGARAN 2020,,0,,"Diva Kurnianingtyas, S Kom",,https://scholar.google.com/citations?hl=id&user=_tSzQnkAAAAJ,"RINGKASAN BPJS Kesehatan (BPJSK) merupakan program pemerintah untuk membantu kebutuhan dasar kesehatan. Hasil evaluasi sistem BPJSK menunjukkan salah satu hambatan yang dihadapi adalah anggaran keuangan yang defisit. Pada penelitian ini diusulkan strategi kebijakan baru dengan mengubah mekanisme rujukan dan besaran premi peserta agar dapat menjaga keberlanjutan keuangan program tersebut. Sistem dinamik (SD) digunakan untuk memodelkan dan mensimulasikan pengaruh sektor fasilitas kesehatan, kepesertaan, dan anggaran keuangan. sehingga memperoleh faktor yang mempengaruhi keberlanjutan sektor finansial pada BPJSK. Kemudian, mengevaluasi dan merencanakan kebijakan yang lebih baik pada tahun selanjutnya. Data historis yang digunakan berasal dari BPJSK mulai tahun 2014 hingga tahun 2018. Validasi model menggunakan uji mean comparison dan uji kondisi ekstrim. Prediksi dampak perubahan strategi kebijakan baik premi maupun biaya kesehatan digunakan untuk mengembangkan model strategi BPJSK dilakukan analisis sensitivitas pada variabel premi dan biaya kesehatan. Analisis ini menghasilkan bahwa perlunya ada perubahan pada variabel premi dan mekanisme rujukan agar terjadi stabilitas pada anggaran keuangan BPJSK. Oleh karena itu, terbentuklah lima model usulan yang terdiri dari usulan pertama (mengubah rujukan berjenjang ke rujukan langsung), usulan kedua (mengubah rujukan berjenjang ke rujukan kombinasi), usulan ketiga (mengubah besaran premi pada model eksisting), usulan keempat (mengubah besaran premi pada model usulan pertama), dan …"

publications/publications_hidayat_nurul.csv

@@ -0,0 +1,159 @@
+Author_Name,Author_Interests,Publication_Title,Year,Citations,Journal,Authors,Publication_Date,Profile_URL,Abstract
+Hidayat Nurul,Expert System,Pemilihan alternatif simplisia menggunakan metode weighted product (wp) dan metode simple additive weighting (saw),2015,18,Journal of Environmental Engineering and Sustainable Technology,"Febrianita Indah Perwitasari, Arief Andy Soebroto, Nurul Hidayat",2015/5/15,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,"Nowadays, people tend to consume organic stuff for meal and medication because of its condition of being secure and inexpensive price. Simplisia is organic material which is not yet processed in order to cure the illness. The part that is used from the whole part to the each piece of simplisia, such as leaves, flowers, fruits, and so on. Simplisia has been being used for solution to the illness, especially at Poli Obat Tradisional RSUD Dr. Soetomo. There are many variants of illness that can be cured by simplisia and there are many variants of simplisia than can be used to cure the illness, which are all usually made the people confused which one is the best variant to cure. Regarding of choosing the alternatives, there is more than one method in Decision Support System that can be used to solve the problem. In this research, there will be two methods that aim at finding the best alternative of simplisia, which are Weighted Product (WP) and Simple Additive Weighting (SAW). Comparison research is used to decide which method as the best method on giving simplisia for the illness. The test scenario is comparing between the result which is given by the system and by the doctor. The accuracy of the result for WP method is 89% and SAW method is 89%."
+Hidayat Nurul,Expert System,Cara Cepat Untuk Mendeteksi Keberadaan Wajah Pada Citra Yang Mempunyai Background Kompleks Menggunakan Model Warna YCbCr dan HSV,2015,11,Jurnal Teknologi Informasi dan Ilmu Komputer,"Nurul Hidayat, Muh Arif Rahman",2015/7/22,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,"AbstrakPendeteksi wajah dari sebuah citra baik berupa gambar diam maupun bergerak merupakan topik penting dan menarik saat ini. Proses deteksi keberadaan wajah ini menjadi dasar dari proses pengenalan wajah yang mempunyai banyak implementasi baik pada bidang keamanan maupun sosial media. Tujuan dari proses deteksi wajah adalah untuk mengetahui apakah ada wajah dari suatu citra, kemudian menemukan letak keberadaan wajah. Pendeteksian wajah adalah tahapan penting dari aplikasi yang memanfaatkan keberadaan wajah pada suatu citra. Implementasinya cukup banyak terutama di bidang biometri keamanan dan sosial media. Riset ini mengusulkan deteksi wajah menggunakan 3 tahapan umum yaitu segmentasi warna kulit manusia, binarisasi dan penentuan region garis serta deteksi wajah menggunakan ruang warna YCbCr dan HSV. Dalam penelitian ini dilakukan deteksi wajah pada 10 citra yang memiliki background yang kompleks. Pendeksian lokasi wajah didasarkan pada temuan hole mata yang simetris. Wajah yang terlalu kecil membuat keberadaan mata hanya terdeteksi sebelah sehingga mengakibatkan wajah tidak terdeteksi. Hasil evaluasi didapatkan tingkat akurasi rata-rata deteksi wajah mencapai 83,4% dengan kecepatan rata-rata 6530 piksel/detik.Kata kunci: Deteksi Wajah, Biometri, Segmentasi, YCbCr, HSV, Region GarisAbstractFace detection of an image either still or moving image is an important and interesting topic today. Face detection process where it became the basis of face recognition process that has many implementations, both in the field of security and social media. The aim of …"
+Hidayat Nurul,Expert System,Penerapan Teorema Bayes Untuk Identifikasi Penyakit Pada Tanaman Kedelai,2013,9,Universitas Brawijaya. Malang.( http://repository. ub. ac. id/47277).(Diunduh pada tanggal 20 Maret 2017 pukul 09.25),"Wisnu Mahendra, Achmad Ridok, Nurul Hidayat",2013/8/21,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,"Kedelai merupakan salah satu komoditi pangan utama di Indonesia. Kebutuhan akan komoditi kedelai terus meningkat dari tahun ke tahun baik sebagai bahan pangan utama, pakan ternak maupun sebagai bahan baku industri skala besar hingga skala kecil. Berbagai upaya telah dilakukan untuk meningkatkan produksi kedelai nasional antara lain dengan penelitian varietas unggul, perluasan areal tanam, dan penyuluhan. Namun dalam proses penanaman kedelai terdapat beberapa kendala yaitu intensitas serangan hama dan penyakit, dan kurangnya tenaga penyuluh pertanian. Dalam mengatasi masalah serangan penyakit pada tanaman kedelai, petani kedelai selaku pihak yang berhubungan secara langsung pada penanaman kedelai perlu untuk mengetahui informasi yang cepat dan akurat terkait jenis penyakit yang menyerang. Sehingga setelah didapatkan informasi penyakitnya maka dapat segera diketahui solusi untuk mengatasi serangan penyakit tersebut. Dengan berkembangnya teknologi informasi, banyak informasi yang dapat diakses secara cepat melalui layanan internet. Kemudahan akses terhadap informasi inilah yang salah satunya dapat digunakan untuk memberikan informasi kepada petani kedelai tentang identifikasi penyakit. Oleh karena itu penulis mencoba memberikan salah satu solusi yang dapat dilakukan untuk membantu petani kedelai dalam mengidentifikasi penyakit tanaman kedelai dengan membuat suatu aplikasi berbasis Web yang dapat diakses oleh seluruh petani kedelai yang terjangkau oleh layanan internet. Aplikasi yang dibuat dapat melakukan identifikasi penyakit berdasarkan gejala yang …"
+Hidayat Nurul,Expert System,PENERAPAN ALGORITMA MODIFIED K-NEAREST NEIGHBOUR (M-KNN) PADA PENGKLASIFIKASIAN PENYAKIT TANAMAN KEDELAI,2014,8,PTIIK Doro,"Arief Andy Soebroto Sofa Zainuddin, Nurul Hidayat",2014,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,Segmentasi Kendaraan Menggunakan Improve Blob Analysis (BA) Pada Video Lalu Lintas,2015,4,Jurnal Teknologi Informasi dan Ilmu Komputer,"Imam Cholissodin, Rina Christanti, Candra Dewi, Nurul Hidayat",2015/2/17,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,"AbstrakPenggunaan citra digital untuk keperluan penelitian sudah banyak dilakukan, salah satunya yaitu segmentasi. Segmentasi berfungsi untuk mendeteksi objek - objek yang terdapat pada citra, sehingga hasil segmentasi sangat penting untuk proses selanjutnya. Pada penelitian ini diusulkan teknik optimasi hasil background subtraction menggunakan kombinasi frame difference (FD) atau difference image dengan filter SDGD dan running average (RA) atau background updating dengan filter SDGD untuk diterapkan pada blob analysis. Alasan utama menggunakan penggabungan kedua metode tersebut adalah karena seringnya terdapat piksel objek yang tidak mampu dideteksi sehingga akan mengurangi tingkat optimasi pengenalan objek. Hasil pengujian akurasi dari 10 data uji yang masing – masing terdiri dari 30 frame menunjukkan bahwa aplikasi ini memiliki nilai akurasi tertinggi yakni 90% untuk pengujian threshold dan 100% untuk pengujian ukuran structure element. Sehingga dapat disimpulkan bahwa aplikasi ini mampu melakukan segmentasi kendaraan dengan baik.Kata kunci: filter SDGD, blob analysis, video lalu lintas, background subtraction.AbstractThe use of digital images for the purposes of research has been often applied, one of them is segmentation. Segmentation is used to detect objects contained in the image, so the segmentation result is very important for further processing. In this study, the results of the optimization technique proposed background subtraction using a combination of frame difference (FD) or a difference image with filter SDGD and running average (RA) or background updating with SDGD filter …"
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR DIAGNOSA PENYAKIT TANAMAN JARAK PAGAR DENGAN METODE FUZZY TSUKAMOTO,2016,2,PTIIK Doro,"M. Ali Fauzi Sari Kusuma Damsuki, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,Integrasi Metode Fuzzy Additive SVM (FASVM) Menggunakan Model Warna YUV-CMY-HSV Untuk Klasifikasi Bibit Unggul Sapi Bali Melalui Citra Digital,2015,2,Jurnal Teknologi Informasi dan Ilmu Komputer,"Imam Cholissodin, Arief Andy Soebroto, Nurul Hidayat",2015/7/22,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,"AbstrakBudidaya sapi sangat identik dengan pemilihan bibit unggul, namun permasalahan yang sering muncul adalah cara mengenali bibit unggul tersebut yang relatif tidak mudah, cenderung membutuhkan waktu cukup lama. Peternak masih sering mengamati warna kulit dengan mata secara langsung, yang cenderung kurang teliti. Sehingga dalam penelitian ini, diusulkan metode dengan menggunakan beberapa model warna yang nantinya sebagai rekomendasi untuk fitur yang optimal dalam sistem. Kemudian metode klasifikasi yang digunakan adalah Fuzzy Additive Support Vector Machine (FASVM). Data yang digunakan didapatkan dari Balai Pembibitan Ternak Unggul (BPTU) Sapi Bali. Dari hasil pengujian didapatkan model warna yang paling optimal dan rata-rata akurasi pada Sapi Betina dan Jantan dengan ukuran citra tertentu. Model warna tersebut sangat dipengaruhi oleh kondisi data citra dan juga banyaknya kelas data.Kata kunci: Sapi Bali, Model warna , Intersection kernel, Fuzzy additive SVM, Sequential training SVM AbstractCattle farming is identical with the selection of seeds, but the problems that often arises is how to recognize quality seeds are relatively easy, tend to take a long time. Breeders still often observe skin color with eyes directly, which tend to be less rigorous. Thus, in this study, the proposed method by using several color models that will be voted on features that are optimal in the system. Then the classification method used is Additive Fuzzy Support Vector Machine (FASVM). The data used was obtained from Livestock Breeding Center for Excellence (BPTU) Bali cattle. From the test results obtained the …"
+Hidayat Nurul,Expert System,Search Engine of Subject Using Error Correction Lavenshtein (Case Study Digital Documents of Al Qu'ran and Hadith),2014,2,Journal of Natural A,"Edy Santoso, Nurul Hidayat",2014,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,"Qur'an and Hadith is the holy book for Muslims as a way of life in everyday life. Qur'an itself consists of 30 juz, 114 letters and approximately 6,666 verses. While the Hadith is also very much making it difficult for Muslims who still lay preacher or a legal basis for the search for subjects interrelated among the verses in the Qur'an and Qur'anic verses linkages and Hadith. Today, existing search engines (Icon Find) but did not do a grouping of words that have been found so that the reader is difficult to understand because the scattered pages of documents. On the other hand people tend to have typing errors to look for a particular subject and if an error occurs writing the words that contain a particular subject was not found. Clerical errors are generally caused by the proximity of the keyboard layout, less adept at using finger, or because the two characters are located confused. Levenshtein algorithm is an algorithm that is reliable and can be used to calculate the relationship between the strings by way of calculating the distance or amount of difference between two strings. With this method the system is expected to have mistyped the recommendations and improvements to search and classify the subject of the search results in a separate sheet that allows the reader. Based on the experimental results generated that included the greater accuracy that the fewer the number of words recommended."
+Hidayat Nurul,Expert System,Named Entity Recognition for Characteristic of Medical Herbs Using Modified HMM Approach,2019,0,Transactions on Machine Learning and Artificial Intelligence,"Lailil Muflikhah, Agung Setiyono, Nurul Hidayat",2019/2/23,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,"The amount of articles in medicinal herbs is very huge. It is performed with unstructured format so that it takes time to get information as reader’s need. Therefore, this research purposes to recognize the name entity of article from internet in order to increase information retrieval or other analysis data purposes. Named entity recognition is one of the goals of information extraction which is to identify the name and characteristics of the herbs. This paper is propose the modified method of Hidden Marcov Model (HMM) with Viterbi algorithm. In this method, it is enclosed gazetteer list for labeling name and location of data training to construct HMM. The data sets are taken from three web sites including: miliaton, aliweb, and plants. As a result, the performance is achieved at average precision value of 0.93, recall of 0.83 and fmeasure of 0.85."
+Hidayat Nurul,Expert System,IMPLEMENTASI METODE PROMETHEE UNTUK PENGAMBILAN KEPUTUSAN PEMILIHAN PEMAIN INTI PADA TIM SEPAK BOLA (STUDI KASUS: SSB PUTRA AREMA MALANG),2017,0,PTIIK Doro,"Randy Cahya W Yuda Kurnia Andrianto, Nurul Hidayat",2017,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI METODE FUZZY-TSUKAMOTO DALAM PENETAPAN HARGA JUAL BANDENG TAMBAK AIR ASIN,2017,0,PTIIK Doro,"Indriati Arif Rahman Hakim K.a, Nurul Hidayat",2017,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,SISTEM PENDUKUNG KEPUTUSAN PENENTUAN JENIS IKAN AIR TAWAR UNTUK USAHA PEMBESARAN MENGGUNAKAN METODE ANALYTIC HIERARCHY,2017,0,PTIIK Doro,"Arief Andy Soebroto Achmad Iman Norrohman, Nurul Hidayat",2017,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PENENTUAN PERINGKAT IDE KREATIF MENGGUNAKAN METODE ELIMINATION ET CHOIX TRADUISANT LA REALITE (ELECTRE) DAN SIMPLE ADDITIVE WEIGHTING (SAW) STUDI KASUS DI PT. PJB UP PAITON,2017,0,PTIIK Doro,"Nurul Hidayat Heru Chrisandi Setiawan, Indriati",2017,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PENENTUAN PEMAIN TIM BASKET MENGGUNAKAN METODE WEIGHTED PRODUCT (WP) - SIMPLE ADDITIVE WEIGHTED (SAW) (STUDI KASUS : TIM PORPROV KOTA PASURUAN),2017,0,PTIIK Doro,"Nurul Hidayat Raditya Alvin Renaldi, Edy Santoso",2017,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI METODE DEMPSTER-SHAFER UNTUK MENDETEKSI KERUSAKAN PADA MESIN PHOTOCOPY,2017,0,PTIIK Doro,"Suprapto Ridhofi Garish I, Nurul Hidayat",2017,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PENENTUAN PEMAIN TIM TENIS LAPANGAN MENGGUNAKAN METODE FUZZY – SIMPLE ADDITIVE WEIGHTING (F-SAW) (STUDI KASUS : UKM UATL UNIVERSITAS BRAWIJAYA),2017,0,PTIIK Doro,"Nurul Hidayat Kikit Maulana Prihantoro Ulil Albab, Edy Santoso",2017,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN KUALITAS CRUDE PALM OIL (CPO) SEBAGAI BAHAN BAKU MINYAK GORENG DENGAN MENGGUNAKAN METODE FUZZY TSUKAMOTO STUDI KASUS PT …,2016,0,PTIIK Doro,"Sutrisno Afi Muftihul Situmorang, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR UNTUK DIAGNOSA HAMA - PENYAKIT PADA TANAMAN KACANG PANJANG DENGAN METODE DEMPSTER-SHAFER,2016,0,PTIIK Doro,"Imam Cholissodin Alfian Mukmin Ali, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN SELEKSI PENERIMA BERAS MASYARAKAT MISKIN (RASKIN) MENGGUNAKAN METODE ANALYTIC HIERARCHY PROCESS-WEIGHTED PRODUCT (AHP-WP) (STUDI KASUS …,2016,0,PTIIK Doro,"Indriati Aula Rieza Syaiful F, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI ANALYTICAL HIERARCHY PROCESS (AHP) DAN GRAPHICAL USER INTERFACE (GUI) PADA PEMILIHAN STARTING LINEUP PEMAIN SEPAK BOLA (Studi Kasus Akademi Arema),2016,0,PTIIK Doro,"Indriati Yogie Meru Kusuma, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PERMODELAN SISTEM PAKAR DIAGNOSA PENYAKIT HIV MENGGUNAKAN METODE CERTAINTY FACTOR,2016,0,PTIIK Doro,"Edy Santoso Riski Aidha Amalia, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,REKOMENDASI TINDAKAN PETANI TERHADAP PENYAKIT PADA TANAMAN TEMBAKAU MENGGUNAKAN METODE AHP - TOPSIS,2016,0,PTIIK Doro,"Indriati Reza Dwi Rahmadian, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR DIAGNOSA PENYAKIT DIABETES MELITUS MENGGUNAKAN METODE AHP-TSUKAMOTO,2016,0,PTIIK Doro,"Edy Santoso Wahyudi Hatiyanto, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN REKOMENDASI PELUANG KERJA BERDASARKAN SERAPAN ALUMNI MENGGUNAKAN METODE AHP - WP [STUDI KASUS POLTEKKES KEMENKES MALANG],2016,0,PTIIK Doro,"Heru Nurwarsito Gilang Rama Hendra, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN (SPK) REKOMENDASI UNTUK MENENTUKAN SUPPLIER KERTAS BAHAN BAKU BUNGKUS ROKOK PADA PERUSAHAAN ROKOK 369 (SAM LIOK KIOE) MENGGUNAKAN METODE …,2016,0,PTIIK Doro,"Dian Eka Ratnawati Agung Mustika Rizki, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR UNTUK MENDIAGNOSA PENYAKIT DEMAM BERDARAH DENGUE MENGGUNAKAN METODE DEMPSTER-SHAFER,2016,0,PTIIK Doro,"Edy Santoso Jun Surya Dhoni R, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR DIAGNOSA HAMA PENYAKIT PADA TANAMAN PEPAYA DENGAN MENGGUNAKAN METODE DEMPSTER-SHAFER,2016,0,PTIIK Doro,"Sutrisno Ahsan Fikri Al Hakim, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PREDIKSI KUALITAS RENDEMEN TEBU DENGAN METODE AHP - SAW,2016,0,PTIIK Doro,Nurul Hidayat dan Edy Santoso Ginanjar Delli Priyo Putro,2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN SELEKSI PENERIMAAN BEASISWA GAKIN MENGGUNAKAN METODE AHP – SAW [STUDI KASUS POLTEKKES TERNATE],2016,0,PTIIK Doro,"Heru Nurwarsito Abdul Kabir Soamole, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN PELUANG KERJA MENGGUNAKAN METODE AHP-PROMETHEE (Studi Kasus: Poltekkes Kemenkes Malang),2016,0,PTIIK Doro,"Edy Santoso Mochamad Adlan Zakariya, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR UNTUK IDENTIFIKASI PENYAKIT PADA TANAMAN KEDELAI MENGGUNAKAN METODE FUZZY K-NEAREST NEIGHBOR,2016,0,PTIIK Doro,"M. Ali Fauzi Romantika Mayang Asri, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,Pemodelan Sistem Pendukung Keputusan Rekomendasi PELUANG KERJA dengan Metode AHP-SAW (STUDI KASUS POLTEKKES KEMENKES MALANG),2016,0,PTIIK Doro,"M. Ali Fauzi Oki Untoro, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR DIAGNOSA PENYAKIT PADA TANAMAN KOPI ARABICA MENGGUNAKAN METODE AHP-SAW,2016,0,PTIIK Doro,"M. Tanzil Furqon Muhammad Harir Muslim, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PERMODELAN SISTEM PAKAR DIAGNOSA PENYAKIT HIV MENGGUNAKAN METODE NAIVE BAYES,2016,0,PTIIK Doro,"Mahendra Data Fery Fatma Rukmana, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PERMODELAN SISTEM PAKAR DIAGNOSA PENYAKIT HIV MENGGUNAKAN METODE FUZZY TSUKAMOTO,2016,0,PTIIK Doro,"Mahendra Data Vika Lailiyah, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PREDIKSI TINGKAT KUALITAS RENDEMEN TEBU DENGAN METODE AHP-TOPSIS,2016,0,PTIIK Doro,"Indriati Brian Anggi Laxmana Putra, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,SISTEM PENDUKUNG KEPUTUSAN DETEKSI DINI PENYAKIT DEMAM BERDARAH MENGUNAKAN METODE AHP-WP,2016,0,PTIIK Doro,"Nurul Hidayat Rizky Ramadhan H P, Marji",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR IDENTIFIKASI HAMA-PENYAKIT PADA TANAMAN KAPAS DENGAN METODE DEMPSTER-SHAFER,2016,0,PTIIK Doro,"Achmad Ridok Dynda Perwary, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR UNTUK DIAGNOSA PENYAKIT TANAMAN JAGUNG MENGGUNKAN METODE MODIFIED K-NEAREST NEIGHBOR (MKNN),2016,0,PTIIK Doro,"M. Tanzil Furqon Riesma Kenny Andhina, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN SELEKSI PENERIMA BERAS MASYARAKAT MISKIN (RASKIN) MENGGUNAKAN METODE ANALYTICAL HIERARCHY PROCESS – THE TECHNIQUE FOR ORDER OF PREFERENCE …,2016,0,PTIIK Doro,"Sutrisno Muhammad Johan Adi Prasetyo, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,SISTEM DIAGNOSA PENYAKIT PADA TANAMAN CABAI MERAH MENGGUNAKAN METODE FUZZY TSUKAMOTO,2016,0,PTIIK Doro,"M. Ali Fauzi Sheila Zivana Lasahido, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN REKOMENDASI PENENTUAN TINGKAT KUALITAS PRODUKTIVITAS AYAM RAS PETELUR DENGAN METODE FUZZY AHP,2016,0,PTIIK Doro,"M. Tanzil Furqon Muhammad Lutvi Syaifudin, Nurul Hidayat,",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI METODE FUZZY INFERENCE SUGENO UNTUK REKOMENDASI KELAYAKAN PENGAJUAN DANA KREDIT SEPEDA MOTOR,2016,0,PTIIK Doro,"Marji Putu Arya Kurnia Yudhanta, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR DIAGNOSIS PENYAKIT PERIODONTAL PADA GIGI DAN MULUT MENGGUNAKAN METODE AHP-SAW,2016,0,PTIIK Doro,"Edy Santoso Muhammad Shalahuddin Munif, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,SISTEM DIAGNOSA PENYAKIT PADA TANAMAN JAGUNG DENGAN MENGGUNAKAN METODE FUZZY TSUKAMOTO,2016,0,PTIIK Doro,"Edy Santoso Fendy Gusta Pradana, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR UNTUK IDENTIFIKASI PENYAKIT JAGUNG DENGAN METODE FUZZY ANALYTICAL HIERARCHY PROCESS,2016,0,PTIIK Doro,"M. Tanzil Furqon Indira Tiara Ayu, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR UNTUK DIAGNOSA PENYAKIT TANAMAN JAGUNG MENGGUNAKAN METODE NAIVE BAYES – CERTAINTY FACTOR,2016,0,PTIIK Doro,"M. Tanzil Furqon Basyiruddin Luthfi, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR DIAGNOSA PENYAKIT HIV MENGGUNAKAN METODE FUZZY SUGENO - CERTAINTY FACTOR,2016,0,PTIIK Doro,"Edy Santoso Luh Kiki Sidhi Cillasavet Dias, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI METODE SUPPORT VECTOR MACHINE UNTUK REKOMENDASI PEMILIHAN TERAPI DEHIDRASI PADA ANAK,2016,0,PTIIK Doro,"Nurul Hidayat Rani Anggi Nilam Sari, Imam Cholissodin",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI METODE CERTAINTY FACTOR PADA REKOMENDASI KEMINATAN LABORATORIUM (STUDI KASUS: PROGRAM STUDI INFORMATIKA / ILMU KOMPUTER PTIIK UNIVERSITAS BRAWIJAYA),2016,0,PTIIK Doro,"Imam Cholissodin Muhammad Fariz Tiowiradin, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI METODE PROFILE MATCHING – WEIGHTED PRODUCT PADA PENYELEKSIAN PENERIMA BANTUAN BERAS UNTUK KELUARGA MISKIN (RASKIN) (STUDI KASUS: KELURAHAN KESATRIAN KOTA MALANG),2016,0,PTIIK Doro,"Nurul Hidayat Mukhammad Zulfikar, Rekyan Regasari Mardi Putri",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI METODE SIMPLE ADDITIVE WEIGHTING - WEIGHTED PRODUCT PADA PENYELEKSIAN PENERIMA BANTUAN BERAS UNTUK KELUARGA MISKIN (RASKIN) (STUDI KASKUS: KELURAHAN KESATRIAN …,2016,0,PTIIK Doro,"Nurul Hidayat Edwin Pratama, Rekyan Regasari Mardi Putri",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR UNTUK IDENTIFIKASI KERUSAKAN PRINTER MENGGUNAKAN METODE DEMPSTER-SHAFER,2016,0,PTIIK Doro,"Marji Idris Mukhni, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,SISTEM DIAGNOSIS PENYAKIT TANAMAN KEDELAI MENGGUNAKAN METODE FUZZY-AHP,2016,0,PTIIK Doro,"Sutrisno Muhammad Haekal, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR UNTUK MENDIAGNOSA PENYAKIT PARU PADA ANAK DENGAN METODE DEMPSTER-SHAFER,2016,0,PTIIK Doro,"Marji Yuangga Dwi Purwita, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI METODE MODIFIED K-NEAREST NEIGHBOR (MK-NN) PADA SISTEM DIAGNOSA PENYAKIT PARU-PARU ANAK,2016,0,PTIIK Doro,"Edy Santoso Deby Faisol Akbar, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN SELEKSI PENERIMA BERAS MISKIN (RASKIN) MENGGUNAKAN METODE FUZZY-AHP DAN TOPSIS (STUDI KASUS : KELURAHAN DONOMULYO KABUPATEN MALANG),2016,0,PTIIK Doro,"Edy Santoso A Fajar Zazuli, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN UNTUK REKOMENDASI PENENTUAN TINGKAT KUALITAS PRODUKTIVITAS AYAM PETELUR DENGAN METODE FUZZY - PROMETHEE,2016,0,PTIIK Doro,"Sutrisno Umar Sagaf, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR UNTUK IDENTIFIKASI KERUSAKAN MOBIL MENGGUNAKAN METODE DEMPSTER-SHAFER,2016,0,PTIIK Doro,"Sutrisno Muhammad Sulton Muttaqin, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR DIAGNOSA HAMA - PENYAKIT PADA TANAMAN KACANG PANJANG MENGGUNAKAN METODE NAIVE BAYES,2016,0,PTIIK Doro,"Sutrisno Rahmawati Purwantiningsih, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,SISTEM PAKAR PENDETEKSI DAN PENANGANAN DINI PENYAKIT KULIT PADA ANAK DENGAN METODE DEMPSTER-SHAFER,2016,0,PTIIK Doro,"Nurul Hidayat Rani Anugrah Wijaya, Rekyan Regasari Mardi Putri",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR DIAGNOSA PENYIMPANGAN TUMBUH KEMBANG ANAK MENGGUNAKAN METODE DEMPSTER-SHAFER,2016,0,PTIIK Doro,"Sutrisno Siti Rahmadini, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR IDENTIFIKASI HAMA PENYAKIT TANAMAN KAPAS DENGAN METODE NAÏVE BAYES,2016,0,PTIIK Doro,"Achmad Ridok Sri Wahyuni, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,"SISTEM PENENTUAN KELAYAKAN KANDANG AYAM BROILER MENGGUNAKAN METODE ANP-TOPSIS (Studi Kasus: PT. Semesta Mitra Sejahtera wilayah Jombang, Kediri, Tulungagung)",2016,0,PTIIK Doro,"Imam Cholissodin Hery Dwi Handoko, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR UNTUK IDENTIFIKASI PENYAKIT PADA TANAMAN TOMAT MENGGUNAKAN METODE FUZZY K-NEAREST NEIGHBOR,2016,0,PTIIK Doro,"Indriati Syela Ukmala, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR DIAGNOSA PENYAKIT MATA MENGGUNAKAN METODE NAIVE BAYES – CERTAINTY FACTOR,2016,0,PTIIK Doro,"Rekyan Regasari Mardi Putri Putri Lestari, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN PENENTUAN SISWA TELADAN MENGGUNAKAN METODE PROFILE MATCHING-ANALYTICAL HIERARCHY PROCESS (STUDY KASUS : SMPN 1 WATES),2016,0,PTIIK Doro,"Dian Eka Ratnawati Bepriandi H C K, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG UNTUK M KEPUTUSAN PENENTUAN KELAYAKAN KANDANG AYAM BROILER DENGAN METODE ANALYTICAL HIERARCHY PROCESS (AHP) SIMPLE ADDITIVE WEIGHTING (SAW) [Studi …,2016,0,PTIIK Doro,"Marji Riza Krusdianto, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR UNTUK MENDIAGNOSA PENYAKIT DIABETES MELLITUS MENGGUNAKAN METODE FORWARD CHAINING - DEMPSTER SHAFER (Studi Kasus: Puskesmas Poncokusumo Kabupaten Malang),2016,0,PTIIK Doro,"Edy Santoso Alvin Chandra Hermawan, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN PENENTUAN KELAYAKAN KANDANG AYAM BROILER DENGAN METODE WEIGHTED PRODUCT (WP) TECHNIQUE FOR ORDER OF PREFERENCE BY SIMILARITY TO IDEAL …,2016,0,PTIIK Doro,"M. Tanzil Furqon Anugrah Ismail, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,OPTIMASI KOMPOSISI PAKAN TERNAK AYAM PEDAGING MENGGUNAKAN ALGORITMA GENETIKA SELEKSI ROULETTE WHEEL,2016,0,PTIIK Doro,"Sutrisno Mellinda Ajeng Jayanti, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,SISTEM PEMILIHAN GURU BERPRESTASI MENGGUNAKAN METODE AHPTOPSIS (Studi Kasus: Dinas Pendidikan Kabupaten Bojonegoro),2016,0,PTIIK Doro,"Indriati Rumekso Uji Swasono, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN MENENTUKAN SISWA TELADAN MENGGUNAKAN METODE FUZZY ANALYTICAL HIERARCHY PROCESS (FAHP) (STUDY KASUS : SMPN 1 WATES),2016,0,PTIIK Doro,"Edy Santoso Harys Setyo Nugroho, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PENCARIAN POLA KECENDERUNGAN PENGAMBILAN MATA KULIAH MENGGUNAKAN ALGORITMA APRIORI (Studi Kasus KRS Mahasiswa Informatika Angkatan 2012 Universitas Brawijaya),2016,0,PTIIK Doro,"M. Tanzil Furqon Muhammad Iqbal, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR DIAGNOSA PENYAKIT MALARIA DENGAN METODE DEMPSTER SHAFER,2016,0,PTIIK Doro,"Marji Fadhillah Aria Digdaya, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR UNTUK MENDIAGNOSA PENYAKIT HEPATITIS DENGAN MENGGUNAKAN METODE FORWARD CHAINING – DEMPSTER SHAFER   Home Archives Volume 8  Number 3,2016,0,PTIIK Doro,Nurul Hidayat dan M. Tanzil Furqon Dian Herman Syah,2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR UNTUK MENGANALISIS KERUSAKAN MESIN PADA KENDARAAN RODA 4 MENGGUNAKAN METODE FORWARD CHAINING – DEMPSTER SHAFER,2016,0,PTIIK Doro,"Achmad Ridok Zainur Rochim Efendi, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN MENENTUKAN SISWA TELADAN MENGGUNAKAN METODE FUZZY TSUKAMOTO - WEIGHTED PRODUCT (STUDY KASUS : SMPN 1 WATES),2016,0,PTIIK Doro,"Sutrisno Muhamad Ari N, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR DIAGNOSA PENYAKIT TANAMAN CABAI MERAH DENGAN METODE AHP-SAW,2016,0,PTIIK Doro,"Marji Muhammad Ali Al Atas, Nurul Hidayat dan",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR UNTUK MENDETEKSI PENYAKIT DIABETES MELITUS MENGGUNAKAN METODE K-NEAREST NEIGHBOUR - CERTAINTY FACTOR,2016,0,PTIIK Doro,"Edy Santoso M Fadjrin Hidayah Tulloh, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR UNTUK MENDETEKSI PENYAKIT DIABETES MELITUS MENGGUNAKAN METODE NAÏVE BAYES - CERTAINTY FACTOR,2016,0,PTIIK Doro,"Edy Santoso Bramantya Reza Anggriawan, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR UNTUK MENDIAGNOSA PENYAKIT PARU PADA ANAK DENGAN METODE NAÏVE BAYES - CERTAINTY FACTOR,2016,0,PTIIK Doro,"Edy Santoso Laksmana Eka Surya, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR DIAGNOSA HAMA - PENYAKIT PADA TANAMAN BAWANG MERAH MENGGUNAKAN METODE NAÏVE BAYES,2016,0,PTIIK Doro,"M. Ali Fauzi Rizki Ristandi, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR UNTUK DIAGNOSA PENYAKIT PADA KUCING MENGGUNAKAN METODE BAYESIAN NETWORK,2016,0,PTIIK Doro,"M. Tanzil Furqon Khirzun Nada Habibi, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,SISTEM DIAGNOSA PENYAKIT PADA TANAMAN JAGUNG DENGAN MENGGUNAKAN METODE FUZZY TSUKAMOTO,2016,0,PTIIK Doro,"Edy Santoso Fendy Gusta Pradana, Nurul Hidayat",2016,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN PENERIMAAN TUTOR BIMBINGAN BELAJAR PLUS ILHAMI MALANG MENGGUNAKAN METODE ANALYTIC HIERARCHY PROCESS-SIMPLE ADDITIVE WEIGHTING (AHP-SAW),2015,0,PTIIK Doro,"Heru Nurwarsito M. Admiral Alfarisi, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR DIAGNOSA HAMA PENYAKIT PADA TANAMAN BAWANG MERAH DENGAN METODE DEMPSTER-SHAFER,2015,0,PTIIK Doro,"Indriati Teguh Adi Gunawan, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR IDENTIFIKASI PENYAKIT TANAMAN TOMAT MENGGUNAKAN METODE FUZZY ANALYTICAL HIERARCHY PROCESS (F-AHP),2015,0,PTIIK Doro,"Sutrisno Dian Malasari, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR UNTUK MENDETEKSI PENYAKIT DOWN SYNDROME PADA ANAK DENGAN MENGGUNAKAN METODE NAÏVE BAYES,2015,0,PTIIK Doro,"Sutrisno Agung Broto Wijoyo, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR DIAGNOSA PENYAKIT DEMAM BERDARAH MENGGUNAKN METODE CERTAINTY FACTOR – WEIGHTED PRODUCT,2015,0,PTIIK Doro,"Edy Santoso Anisah, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN KOMPOSISI PENGADAAN ALAT KONTRASEPSI MENGGUNAKAN METODE FUZZY MAMDANI – WEIGHTED PRODUCT (STUDI KASUS : BPPKBD NGANJUK),2015,0,PTIIK Doro,"Marji Yudhistira W P, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN PENENTUAN LINE UP CABANG OLAHRAGA FUTSAL MENGGUNAKAN METODE WEIGHTED PRODUCT (WP) – SIMPLE ADDITIVE WEIGHTING (SAW) (STUDI KASUS : HEFOTRIS …,2015,0,PTIIK Doro,"Achmad Ridok Hutamaning Margo Raharjo, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN KOMPOSISI PENGADAAN ALAT KONTRASEPSI MENGGUNAKAN METODE FUZZY TSUKAMOTO-SIMPLE ADDITIVE WEIGTHING (SAW) (STUDY KASUS …,2015,0,PTIIK Doro,"M. Tanzil Furqon Setyo Ngesti Rahayu, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN PENENTUAN LINE UP DALAM CABANG OLAHRAGA FUTSAL DENGAN MENGGUNAKAN METODE AHP-SAW (Studi Kasus: Hefotris FILKOM UB),2015,0,PTIIK Doro,"Edy Santoso Johan Ismail, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN JUMLAH PRODUKSI ALAT KONTRASEPSI MENGGUNAKAN METODE FUZZY TSUKAMOTO - ANALYTICAL HIERARCHY PROCESS (AHP) (STUDY KASUS …,2015,0,PTIIK Doro,"Indriati Angga Andika Kandi, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,DIAGNOSIS PENYAKIT TANAMAN TOMAT MENGGUNAKAN ALGORITMA MODIFIED KNEAREST NEIGHBOR (MKNN),2015,0,PTIIK Doro,"Nurul Hidayat Prasiwi Meilida Basuki, Marji",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR DIAGNOSA PENYAKIT KULIT PADA ANAK MENGGUNAKAN METODE FORWARD CHAINING-CERTAINTY FACTOR,2015,0,PTIIK Doro,"M. Tanzil Furqon Maria Stevani.s, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN REKOMENDASI PELUANG KERJA BERDASARKAN SERAPAN ALUMNI MENGGUNAKAN METODE GABUNGAN AHP-TOPSIS [Studi Kasus Poltekkes Kemenkes Malang],2015,0,PTIIK Doro,"Heru Nurwarsito Dharma Pandu Kresnawan, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN PENENTUAN STARTING LINE UP CABANG OLAHRAGA FUTSAL DENGAN METODE AHP-WP (Studi Kasus: HEFOTRIS FILKOM UB),2015,0,PTIIK Doro,"Achmad Ridok Irvan Kidisetianto, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR DIAGNOSA PENYAKIT TANAMAN KOPI ARABICA MENGGUNAKAN METODE NAIVE BAYES,2015,0,Doro Jurnal,"M. Tanzil Furqon Yasrifan Mahzar N, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR DIAGNOSA PENYAKIT DEMAM BERDARAH MENGGUNAKAN METODE NAIVE BAYES DAN CERTAINTY FACTOR,2015,0,PTIIK Doro,"Edy Santoso Bally Ayu Pertiwi, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR DIAGNOSA PENYAKIT DEMAM BERDARAH MENGGUNAKAN METODE NAIVE BAYES – WEIGHTED PRODUCT,2015,0,PTIIK Doro,"Edy Santoso Desyy Rizky Korniasari, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,SISTEM REKOMENDASI PEGAWAI MENGGUNAKAN METODE SVM dan TOPSIS JURNAL INTERNASIONAL,2015,0,PTIIK Doro,"Nurul Hidayat M. Syamsul Arif H., Imam Cholissodin",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI METODE CERTAINTY FACTOR PADA DIAGNOSIS PENYAKIT TANAMAN JAGUNG,2015,0,PTIIK Doro,"Arief Andy Soebroto Razaq Aulia Majid, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR DIAGNOSA PENYAKIT JAGUNG MENGGUNAKAN METODE CLASSICAL PROBABILITY,2015,0,PTIIK Doro,"Arief Andy Soebroto R. Mohammad Dwi Prasetyo G., Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI METODE FUZZY WEIGHTED PRODUCT UNTUK DIAGNOSIS PENYAKIT TANAMAN JAGUNG,2015,0,PTIIK Doro,"Rekyan Regasari Mardi Putri Hernawan Adi Saputro, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR DIAGNOSA DEMAM PADA BALITA BERBASIS WEB MENGGUNAKAN METODE CERTAINTY FACTOR,2015,0,PTIIK Doro,"Nurul Hidayat Ilma Wihastika Nur I, Candra Dewi",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI METODE SIMPLE ADDITIVE WEIGHTING (SAW) UNTUK SELEKSI PENERIMAAN CALON BRIGADIR POLRI DI WILAYAH KABUPATEN PROBOLINGGO,2015,0,PTIIK Doro,Nurul Hidayat dan Edy Santoso Sonia Alfa Pradibta,2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR DIAGNOSA PENYAKIT TANAMAN KOPI ARABIKA DENGAN METODE FUZZY K-NN,2015,0,PTIIK Doro,"Edy Santoso Anggi Yhurinda Perdana P, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI METODE CERTAINTY FACTOR PADA SISTEM PAKAR DIAGNOSA PENYIMPANGAN TUMBUH KEMBANG ANAK,2015,0,PTIIK Doro,"Nurul Hidayat Arienta Ramadhaniar, Edy Santoso",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI METODE AHP – FUZZY TOPSIS UNTUK REKOMENDASI PENENTUAN TINGKAT KUALITAS PRODUKTIVITAS AYAM RAS PETELUR,2015,0,PTIIK Doro,"Nurul Hidayat Vicky Hardian Kusuma Candra, Imam Cholissodin",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,Sistem Pakar Diagnosa Penyakit Tanaman Kopi Arabica Dengan Metoe Fuzzy-AHP,2015,0,PTIIK Doro,"Dian Eka Ratnawati Fajar Prasetyawan, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR DIAGNOSA PENYAKIT TANAMAN CABAI MERAH DENGAN METODE FUZZY K-nearest neighbor (FK-NN),2015,0,PTIIK Doro,"Marji Dyah Puspitasari, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN PENENTUAN PEMAIN UTAMA TIM BOLA VOLI MENGGUNAKAN AHP - PROMETHEE,2015,0,PTIIK Doro,Rekyan Regasari Mardi Putri dan Nurul Hidayat Muhamad Faruk Farozi,2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,DIAGNOSA PENYAKIT TANAMAN CABAI MERAH MENGGUNAKAN METODE MK-NN,2015,0,PTIIK Doro,"Dian Eka Ratnawati Satya Wiraga, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,DIAGNOSA PENYAKIT TANAMAN KOPI ARABIKA DENGAN METODE MODIFIED KNEAREST NEIGHBOR (MK-NN),2015,0,PTIIK Doro,"Dian Eka Ratnawati Ryan Hendy Septianto, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,SISTEM PENDUKUNG KEPUTUSAN DALAN PEMILIHAN KEMINATAN MENGGUNAKAN METODE FUZZY SIMPLE ADDITIVE WEIGHTED (STUDI KASUS: PROGRAM STUDI INFORMATIKA FAKULTAS ILMU KOMPUTER …,2015,0,PTIIK Doro,"Nurul Hidayat Hervin Nurcahyana, Dian Eka Ratnawati",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,STUDI KOMPARASI METODE WEIGHTED PRODUCT (WP) DENGAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) UNTUK PEMILIHAN ALTERNATIF SIMPLISIA,2015,0,PTIIK Doro,"Nurul Hidayat Febrianita Indah Perwitasari, Arief Andy Soebroto",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,ENSEMBLE MACHINE LEARNING UNTUK REKOMENDASI PENILAIAN KINERJA GURU BERBASIS WEIGHTED PRODUCT (STUDI KASUS SEKOLAH DASAR DI KECAMATAN ROGOJAMPI),2015,0,PTIIK Doro,"Nurul Hidayat Rifqi Kurniawan, Imam Cholissodin",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR DIAGNOSA PENYAKIT TANAMAN CABAI MERAH DENGAN METODE FUZZY-AHP,2015,0,PTIIK Doro,"Edy Santoso Ryan Ramadhan, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,SISTEM PAKAR IDENTIFIKASI HAMA DAN PENYAKIT TANAMAN TEBU MENGGUNAKAN METODE FUZZY-ANALYTIC HIERARCHY PROCCES S(F-AHP),2015,0,PTIIK Doro,"Nurul Hidayat Daria Anggraeni, Arief Andy Soebroto",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,SISTEM PENDUKUNG KEPUTUSAN PENERIMAAN PEGAWAI MKS (MIKRO KREDIT SALES) MENGGUNAKAN METODE FUZZY SIMPLE ADDITIVE WEIGHTING (Studi Kasus: Bank Mandiri Cab. Tulungagung),2015,0,PTIIK Doro,"Nurul Hidayat Novie Cyntha Dewi Hakim, Arief Andy Soebroto",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,SISTEM PENDUKUNG KEPUTUSAN SELEKSI PENERIMAAN PESERTA DIDIK BARU DENGAN METODE ELIMINATION ET CHOIX TRADUISANT LA REALITE - SIMPLE ADDITIVE WEIGHTING (STUDI KASUS: SMP …,2015,0,PTIIK Doro,"Nurul Hidayat Fridha Agustina, Arief Andy Soebroto",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,SISTEM PENDUKUNG KEPUTUSAN SELEKSI PENERIMAAN PEGAWAI MIKRO KREDIT SALES (MKS) DENGAN METODE ANALYTICAL HIERACHY PROCESS – WEIGHTED PRODUCT (AHP-WP) (Studi Kasus: Bank Mandiri …,2015,0,PTIIK Doro,"Nurul Hidayat Geby Firdana, Arief Andy Soebroto",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN PENENTUAN STARTINGLINE UPPEMAIN DALAM CABANG OLAHRAGA FUTSAL DENGAN MENGGUNAKAN METODE WEIGHTED PRODUCT-TOPSIS (Studi Kasus: Hefotris …,2015,0,PTIIK Doro,"Achmad Ridok Ganda Neswara, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,SISTEM PENDUKUNG KEPUTUSAN SELEKSI PENERIMAAN PEGAWAI MKS (MIKRO KREDIT SALES) DENGAN METODE ANALYTICAL HIERARCHY PROCESS (AHP) – PREFERENCE RANKING ORGANIZATION METHOD FOR …,2015,0,PTIIK Doro,"Nurul Hidayat Rhizekha Dwi Wulandari, Arief Andy Soebroto",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,SISTEM PENDUKUNG KEPUTUSAN PENENTUAN OBAT PERAWATAN KULIT WAJAH MENGGUNAKAN METODE FUZZY-SIMPLE ADDITIVE WEIGHTING (F-SAW),2015,0,PTIIK Doro,"Nurul Hidayat Nurul Fitria K., Arief Andy Soebroto",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PAKAR IDENTIFIKASI PENYAKIT TANAMAN TOMAT MENGGUNAKAN METODE AHP-SAW,2015,0,PTIIK Doro,"Edy Santoso Ika Khoirun Nisak, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PEMODELAN SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN LINE UP CABANG OLAHRAGA FUTSAL DENGAN METODE AHP-TOPSIS (STUDI KASUS HEFOTRIS FILKOM UB),2015,0,PTIIK Doro,"Edy Santoso Bogi Farizna Junior, Nurul Hidayat",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI METODE AHP-SVM UNTUK KLASIFIKASI PENERIMA BERAS MASYARAKAT MISKIN (RASKIN) (STUDI KASUS KELURAHAN RONGGOMULYO KABUPATEN TUBAN),2015,0,PTIIK Doro,"Nurul Hidayat Kiki Aprilia Puspitaningrum, Imam Cholissodin",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI ALGORITMA FUZZY K-NEAREST NEIGHBOR (FK-NN) PADA DETEKSI POTENSI BENCANA ALAM TSUNAMI,2015,0,Doro Jurnal,"Nurul Hidayat Faris Fitrianto, Rekyan Regasari Mardi Putri",2015,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN SIMPLISIA NABATI TERHADAP INDIKASI GANGGUAN KESEHATAN MENGGUNAKAN METODE ELECTRE I-TOPSIS,2014,0,PTIIK Doro,Arief Andy Soebroto dan Nurul Hidayat Triwahyudi Suprayogi,2014,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN SIMPLISIA NABATI TERHADAP INDIKASI GANGGUAN KESEHATAN MENGGUNAKAN METODE Analytic Hierarchy Process - The Technique for Order of Preference …,2014,0,PTIIK Doro,"Nurul Hidayat Oksi Iranosa, Arief Andy Soebroto",2014,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI METODE ANALYTIC HIERARCHY PROCESS - SIMPLE ADDITIVE WEIGHTING PADA PEMILIHAN MAHASISWA RAYA (STUDI KASUS: PEMIRA UNIVERSITAS BRAWIJAYA 2013),2014,0,PTIIK Doro,"Budi Darma Setiawan Nailah Husna, Nurul Hidayat",2014,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI ALGORITMA FUZZY K-NEAREST NEIGHBOR (F-KNN) UNTUK MENGETAHUI TINGKAT RESIKO PENYAKIT GAGAL GINJAL,2014,0,PTIIK Doro,"Nurul Hidayat Nurul Maghfirah, Candra Dewi",2014,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI ALGORITMA FUZZY C-MEANS UNTUK PENGELOMPOKKAN TINGKAT PENYAKIT ANEMIA,2014,0,PTIIK Doro,Mardji dan Nurul Hidayat Bayu Wiradarma,2014,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PENERAPAN METODE FUZZY AHP DALAM SELEKSI PEMAIN SEPAK BOLA,2014,0,PTIIK Doro,"Nurul Hidayat Dhimas Yhudo Pratomo, Arief Andy Soebroto",2014,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI ALGORITMA GENETIKA PADA SISTEM PENYUSUNAN JADWAL KRS BERBASIS WEBSITE DI PROGRAM TEKNOLOGI INFORMASI DAN ILMU KOMPUTER (PTIIK),2014,0,PTIIK Doro,"Nurul Hidayat Fariz Rohmansyah, Arief Andy Soebroto",2014,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,OPTIMASI PENJADWALAN MATA PELAJARAN MENGGUNAKAN GENETIC ALGORITHM (Studi Kasus: Madrasah Tsanawiyah Negeri Bangil),2014,0,PTIIK Doro,"Rekyan Regasari Mardi Putri Muhammad Faris, Nurul Hidayat",2014,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI ALGORITMA FUZZY C-MEANS UNTUK PEMBANGKITAN ATURAN FUZZY PADA PENGELOMPOKAN TINGKAT RISIKO PENYAKIT KANKER PAYUDARA,2014,0,PTIIK Doro,"Arief Andy Soebroto Ely Ratna Sayekti, Nurul Hidayat",2014,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI FUZZY INFERENCE SYSTEM MAMDANI UNTUK MENGIDENTIFIKASI LEARNING DISABILITY PADA ANAK,2014,0,PTIIK Doro,"Nurul Hidayat Dwi Fetiria Ningrum, Rekyan Regasari Mardi Putri",2014,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PENERAPAN METODE NEIGHBOR WEIGHTED-K NEAREST NEIGHBOR (NW-KNN) PADA PENGKLASIFIKASIAN SPAM EMAIL,2014,0,PTIIK Doro,"Nurul Hidayat Rahmi Pratiwi, Achmad Ridok",2014,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI METODE FUZZY-AHP UNTUK REKOMENDASI SELEKSI PENERIMAAN ANGGOTA BARU PADUAN SUARA (STUDI KASUS: PADUAN SUARA MAHASISWA UNIVERSITAS BRAWIJAYA),2014,0,PTIIK Doro,"Candra Dewi Nania Nuzulita, Nurul Hidayat",2014,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,Steganografi Ciphertext AES 256 pada Citra Digital Menggunakan Metode Least Significant Bit (LSB),2013,0,PTIIK Doro,"Nurul Hidayat Abhimata Ar Rasyiid, Edy Santoso",2013,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,Implementasi Metode TOPSIS (Technique For Order Preference By Similarity To Ideal Solution) dalam Penjurusan pada Sekolah Menengah Atas,2013,0,PTIIK Doro,"Nurul Hidayat Adityaranda Satriawan, Rekyan Regasari Mardi Putri",2013,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI METODE LINEAR PROGRAMMING UNTUK PERENCANAAN JUMLAH PELAKSANAAN KEGIATAN PUSKESMAS,2013,0,PTIIK Doro,"Nurul Hidayat Haryo Hanindyo, Rekyan Regasari Mardi Putri",2013,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,IMPLEMENTASI FUZZY INFERENCE MACHINE UNTUK MENGIDENTIFIKASI PENYAKIT PADA TANAMAN KEDELAI,2013,0,PTIIK Doro,"Nurul Hidayat Andhika Wanda Putra, Mardji",2013,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,Penerapan Metode Classical Probability untuk Diagnosa Penyakit Tanaman Kedelai Berbasis Web,2013,0,PTIIK Doro,"Nurul Hidayat Rado Anum, Mardji",2013,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,Penerapan Metode Classical Probability untuk Diagnosa Penyakit Tanaman Kedelai Berbasis Web,2013,0,PTIIK Doro,"Nurul Hidayat Rado Anum, Mardji",2013,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PENERAPAN TEOREMA BAYES UNTUK IDENTIFIKASI PENYAKIT PADA TANAMAN KEDELAI,2013,0,Doro Jurnal,"Nurul Hidayat Wisnu Mahendra, Achmad Ridok",2013,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,PENGKLASIFIKASIAN JURNAL ILMIAH BERBAHASA INGGRIS MENGGUNAKAN WEIGHTED ONTOLOGY,2013,0,PTIIK Doro,"Nurul Hidayat Ardhi Priagung, Lailil Muflikhah",2013,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,ENKRIPSI CITRA DIGITAL MENGGUNAKAN ALGORITMA MODIFIED RIVEST CODE 6,2013,0,PTIIK Doro,"Nurul Hidayat Pompy H, Mardji",2013,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,Implementasi Metode Naive Bayes untuk Klasifikasi Kenaikan Grade Karyawan pada Fuzzyfikasi Data Kinerja Karyawan (Studi Kasus PT PJB UP Brantas),2013,0,PTIIK Doro,"Rekyan Regasari Mardi Putri Yoga Agung Baktiar, Nurul Hidayat",2013,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,Implementasi Metode Growcut pada Preprocessing Seam Carving,2013,0,PTIIK Doro,"Nurul Hidayat Akhmad Firdausi, Edy Santoso",2013,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,Clustering Penggunaan Energi Listrik Pelanggan PLN Menggunakan Algoritma Fuzzy C-Means,2013,0,PTIIK Doro,"Nurul Hidayat Briant Saputro, Mardji",2013,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,
+Hidayat Nurul,Expert System,Penerapan Algoritma Genetik Dua Populasi Pada Kasus Transportasi Dua Tahap (Pada Studi Kasus Distribusi Susu Fermentasi Pada Perusahaan XYZ di Pulau Jawa),2012,0,Jurnal POINTER,"Kusuma Ari Prabowo, Achmad Ridok, Nurul Hidayat",2012/2/18,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,"Algoritma genetik adalah suatu metode algoritma optimalisasi dan pencarian yang didasarkan pada prinsip genetika dan seleksi natural (Haupt. 2004). Pada penelitian ini, akan dibangun sebuah aplikasi optimasi pencarian rute pada transportasi dua tahap yang diterapkan pada studi kasus distribusi produk susu fermentasi pada perusahaan XYZ menggunakan algoritma genetik dua populasi. Algoritma genetik dua populasi, adalah suatu algoritma genetik yang membentuk dua populasi sebagai populasinya. Populasi tersebut dibagi dalam populasi elit dan umum, dimana individu yang terdapat pada populasi elit adalah suatu individu dengan nilai fitness tertinggi dan individu pada populasi umum dengan nilai fitness yang lebih rendah (Martikainen dan Ovaska, 2006). Seperti pada proses genetika, algoritma genetik memiliki operator genetik yang digunakan dalam proses regenetik. Pada penelitian ini …"
+Hidayat Nurul,Expert System,Komputasi Frekuensi Kebersamaan Data Berdasarkan Klaster Pembentuknya,2012,0,Jurnal POINTER,"Edy Santoso, Nurul Hidayat",2012/2/18,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,"Salah satu cara untuk mengetahui kemiripan rekord data adalah dengan klastering. Pada metode klastering, dengan jumlah klaster yang sama, antara metode yang satu dengan yang lain kemungkinan menghasilkan struktur klaster yang berbeda. Dengan adanya perbedaan struktur tersebut dimungkinkan dua buah rekord data dengan suatu metode klaster berada dalam suatu klaster, tetapi dengan metode yang lain berada pada klaster yang berbeda."
+Hidayat Nurul,Expert System,Image Resizing Menggunakan Algoritma Seam Carving dengan Menggabungkan Dynamic Programming dan Stochastic Path,2008,0,,AdhieIndi Arysanto,2008/5/15,https://scholar.google.com/citations?hl=id&user=uRRIdpUAAAAJ,"Pada April 2007, Ariel Shamir dan Shai Avidan menemukan dan mempublikasikan algoritma seam carving . Kelebihan dari algoritma ini adalah pada saat ukuran sebuah citra diubah, dimana perubahan ukuran citra tersebut akan mengubah perbandingan panjang dan lebar. Seam carving dapat menjaga agar objek utama dalam citra tetap utuh, baik dengan atau tanpa bantuan user . Masalah yang timbul adalah operator pada penelitian terdahulu menggunakan gradient magnitude dan histogram of oriented gradients dimana hanya 2 piksel yang paling berperan dalam menentukan sebuah tepi. Kedua operator ini sangat sensitif terhadap adanya gangguan pada citra ( noise ), karena hanya sedikit jumlah piksel yang dilibatkan untuk memperhitungkan gradien (Milan dkk., 1993). Selain itu, menurut Hector Yee (2007) penggunaan algoritma dynamic programming seringkali menimbulkan artifact (pembentukan/perubahan objek). Penggunaan metode stochastic path dengan membuat 10.000 seam secara acak dinilai Hector Yee dapat memberikan hasil yang lebih baik (Hector. 2007). Namun, dengan metode tersebut ukuran citra akan mempengaruhi kualitas hasil resizing . Hal ini dikarenakan jumlah seam yang dibuat akan tetap meskipun ukuran citra bervariasi. Oleh karena itu dalam tugas akhir ini akan dilakukan modifikasi dari algoritma seam carving dengan cara menggabungkan algoritma dynamic programming dengan stochastic path dan mengganti operator gradient magnitude dengan operator Laplacian of Gaussian serta melakukan evaluasi terhadap keutuhan objek utama citra hasil dengan cara membandingkan …"

publications/publications_irawati_nurmala_sari.csv

@@ -0,0 +1,17 @@
+Author_Name,Author_Interests,Publication_Title,Year,Citations,Journal,Authors,Publication_Date,Profile_URL,Abstract
+Irawati Nurmala Sari,Image Processing; Image Inpainting; Deep Learning,Interactive image inpainting of large-scale missing region,2021,14,IEEE Access,"Irawati Nurmala Sari, Emiko Horikawa, Weiwei Du",2021/4/12,https://scholar.google.com/citations?hl=id&user=d4vypz8AAAAJ,"Image inpainting is a challenging reconstruction of the damaged image in photography, especially for more valued artwork than before. The damages are mostly caused by scratches and worn out, so they cannot be easily fixed physically. Thus, many scientists proposed sophisticated methods for restoring the damaged image into a new one similar to an original image. However, these methods have not solved the problem effectively if the missing region is large. In this paper, we focus on how to restore a large missing region in image inpainting. This algorithm is composed of two steps: structure propagation and color propagation. In structure propagation, we segment a large region (non-homogeneous) into several small regions (homogeneous) based on the salient structure of missing region. Then, we applied a simple pixel-based inpainting method called the Fast Marching Method (FMM) to fill in the missing …"
+Irawati Nurmala Sari,Image Processing; Image Inpainting; Deep Learning,Structure-texture consistent painting completion for artworks,2023,7,Ieee Access,"Irawati Nurmala Sari, Weiwei Du",2023/3/6,https://scholar.google.com/citations?hl=id&user=d4vypz8AAAAJ,"Image completion techniques have made rapid and impressive progress due to advancements in deep learning and traditional patch-based approaches. The surrounding regions of a hole played a crucial role in repairing missing areas during the restoration process. However, large holes could result in suboptimal restoration outcomes due to complex textures causing significant changes in color gradations. As a result, they led to errors such as color discrepancies, blurriness, artifacts, and unnatural colors. Additionally, recent image completion approaches focused mainly on scenery and face images with fewer textures. Given these observations, we present a structure-texture consistent completion approach for filling large holes with detailed textures. Our method focuses on improving image completion in the context of artworks, which are expressions of creativity and often have more diverse structures and …"
+Irawati Nurmala Sari,Image Processing; Image Inpainting; Deep Learning,Image inpainting using clustered planar structure guidance,2021,6,,"Emiko Horikawa, Irawati Nurmala Sari, Weiwei Du",2021/6/20,https://scholar.google.com/citations?hl=id&user=d4vypz8AAAAJ,"This paper presents an effective method using clustered planar structure guidance for image inpainting. Our method concerns restoring the unknown area by clustering structures of related planes. It is employed to obtain precisely similar structures in the surrounding area of missing regions. The approach of our work contains four essential steps: Planar Guidance, Clustering Structures, Feature Localization, and Patch Matching. According to perspective scenes, we first extract vanishing points (vp1, vp2, and vp3) using RAndom SAmple Consensus (RANSAC) algorithm as planar guidance. Then, we cluster the structure lines of each planar into more categories using the integration between K-means++ and Elbow method. Gaussian filter and Hadamard products blend among structure categories in the feature localization. This feature position propagates the surrounding structure information into unknown areas. For …"
+Irawati Nurmala Sari,Image Processing; Image Inpainting; Deep Learning,Pengaruh penambahan konsentrat protein ikan gabus (channa striatus) terhadap mutu kwetiau,2015,6,JOM,"Wiwi Solvia Siahaan, IN Sari, S Loekman",2015,https://scholar.google.com/citations?hl=id&user=d4vypz8AAAAJ,
+Irawati Nurmala Sari,Image Processing; Image Inpainting; Deep Learning,Depth map estimation of single-view image using smartphone camera for a 3-dimension image generation in augmented reality,2023,5,,"Jun’Nosuke Takarabe, Irawati Nurmala Sari, Weiwei Du",2023/6/30,https://scholar.google.com/citations?hl=id&user=d4vypz8AAAAJ,"Immersive experience exhibits such as augmented reality (AR) are a way to enjoy museums and art galleries because this way not only may update information easily but also does not require physical existence. However, 3-dimension (3D) images of AR are difficult to apply with one image as depth information either is unknown, or is cost and time consuming. This paper designs a depth map estimation method from single-view image by using smartphone camera to generate a 3D image in AR. Some functions are added into AR Depth Lab [7] in a personal computer (PC). Then, the improve AR Depth Lab [7] is installed into the smartphone. The depth map can be obtained by using patch-based depth estimation [8] and saved into the smartphone. The appropriate parameters of the proposal such as patch size, the cropped image size and the appropriate image models are set by experiments."
+Irawati Nurmala Sari,Image Processing; Image Inpainting; Deep Learning,Image inpainting using orthogonal viewpoints and structure consistency in Manhattan World,2021,5,,"Irawati Nurmala Sari, Yuto Urano, Weiwei Du",2021/6/20,https://scholar.google.com/citations?hl=id&user=d4vypz8AAAAJ,"This paper proposes a fast and straightforward method for restoring a damaged hole in the Manhattan world, which has three orthogonality with one unknown camera to produce a perspective view that can foreshorten planes of similar structure. There are two essential guidance for image inpainting algorithms: Orthogonal Viewpoints and Structure Consistency. First, due to perspective view, we build orthogonal viewpoints from an image by determining the vanishing points of different planes. Second, we present structure consistency for reconstructing unknown regions of possibly foreshortened planes due to the displacement vector of repeated structures in perspective planes. For image inpainting, PatchMatch [1] algorithm handles matching structures based on two prior-primary guided methods. Our experiment results prove that our approach completes image inpainting in challenging scenes, such as perspective …"
+Irawati Nurmala Sari,Image Processing; Image Inpainting; Deep Learning,Rancang Bangun Penghitung Benih Ikan Menggunakan Binary Thresholding pada Raspberry Pi secara Real Time,2017,5,Jurnal Informatika Polinema,"Irawati Nurmala Sari, Vivid Ichtarosa Arinda",2017/11/1,https://scholar.google.com/citations?hl=id&user=d4vypz8AAAAJ,"Benih yang baru dipanen biasanya akan menurun kondisinya. Untuk memulihkannya ada beberapa cara salah satunya adalah menampung benih di dalam wadah penampungan sementara ketika panen dilakukan. Selain ditampung, benih juga harus dihitung untuk mengetahui jumlahnya. Perhitungan juga harus dilakukan dengan cepat dan tepat agar benih tidak menjadi lemah, lalu mati. Selama ini petani ikan masih melakukan perhitungan benih secara manual yaitu dengan menghitung satu per satu atau menggunakan volume (gelas). Sehingga selain memakan waktu yang lama, benih ikan terkadang stress dikarenakan perhitungan yang masih manual. Penelitian ini mendesain dan mengembangkan alat yang mampu menghitung benih ikan dengan mengimplementasikan pengolahan citra sebagai solusi untuk mengatasi permasalahan para petani ikan. Sistem yang dirancang dan diimplementasikan menggunakan HTML, Python, serta pengolahan citra yang menggunakan metode Thresholding, Morphology, serta pelabelan. Sistem ini diterapkan secara real time, serta dapat menghitung objek yang mendekati perhitungan yang sebenarnya. Sistem ini telah diuji menggunakan 4 data set yaitu benih yang diuji tiap kelipatan 10 dan berakhir pada pengujian 40 benih ikan. Tingkat keakuratan tertinggi mencapai 99.9977% untuk pengujian perhitungan 40 benih."
+Irawati Nurmala Sari,Image Processing; Image Inpainting; Deep Learning,Rancang Bangun Aplikasi Game Edukasi Pakaian Adat Suku Batak ‘Ulos’ Pada Platform Android,2015,5,Univ. Udayana,IN Sari,2015,https://scholar.google.com/citations?hl=id&user=d4vypz8AAAAJ,
+Irawati Nurmala Sari,Image Processing; Image Inpainting; Deep Learning,Edge-enhanced GAN with vanishing points for image inpainting,2022,4,,"Kei Masaoka, Irawati Nurmala Sari, Weiwei Du",2022/7/4,https://scholar.google.com/citations?hl=id&user=d4vypz8AAAAJ,"Reconstructing the damaged images with perspective views has an extensive range in the field of image inpainting. However, most existing methods generated inadequately realistic restored images. Accomplishing this problem, we propose an edge-enhanced image generation model considering viewpoints. Our method applies edge map information to guide image generation based on the perspective views of an image using vanishing points detection. Texture synthesis will be presented as post-processing to complete the remaining missing regions. Experiment shows that our approach can generate perspective images with convincing details, such as indoor and outdoor facades."
+Irawati Nurmala Sari,Image Processing; Image Inpainting; Deep Learning,Image inpainting using automatic structure propagation with auxiliary line construction,2022,4,,"Yuto Urano, Irawati Nurmala Sari, Weiwei Du",2022/7/4,https://scholar.google.com/citations?hl=id&user=d4vypz8AAAAJ,"Existing image inpainting methods used traditional and deep learning methods to restore a large missing region in the damaged image. This often leads to color discrepancy and blurriness. Pre-processing of prior line detection by user assistance is usually employed to reduce the blurry of center region by segmenting the large region into more minor. However, it operates manually, which is time-consuming. This paper introduces a technique to generate two-line types: penetrator and interactor in constructing auxiliary lines as guidance. These lines assist structure propagation established automatically, while the remaining small regions are filled by texture propagation. Experiments on large regular masks demonstrate that our proposed approach generates higher-quality results than other methods."
+Irawati Nurmala Sari,Image Processing; Image Inpainting; Deep Learning,Human pose tracking using online latent structured support vector machine,2017,3,,"Kai-Lung Hua, Irawati Nurmala Sari, Mei-Chen Yeh",2017,https://scholar.google.com/citations?hl=id&user=d4vypz8AAAAJ,"Tracking human poses in a video is a challenging problem and has numerous applications. The task is particularly difficult in realistic scenes because of several intrinsic and extrinsic factors, including complicated and fast movements, occlusions and lighting changes. We propose an online learning approach for tracking human poses using latent structured Support Vector Machine (SVM). The first frame in a video is used for training, in which body parts are initialized by users and tracking models are learned using latent structured SVM. The models are updated for each subsequent frame in the video sequence. To solve the occlusion problem, we formulate a Prize-Collecting Steiner tree (PCST) problem and use a branch-and-cut algorithm to refine the detection of body parts. Experiments using several challenging videos demonstrate that the proposed method outperforms two state-of-the-art methods."
+Irawati Nurmala Sari,Image Processing; Image Inpainting; Deep Learning,Vanishing points detection with line segments of gaussian sphere,2023,2,,"Kei Masaoka, Irawati Nurmala Sari, Weiwei Du",2023/6/30,https://scholar.google.com/citations?hl=id&user=d4vypz8AAAAJ,"Depth estimation using vanishing points is important in computer vision and has been widely used in various applications such as robotics and autonomous driving. A vanishing point is a point in the image where parallel lines appear to converge to a single point in 3D space. The detection of vanishing points in images plays a crucial role in estimating the depth of a scene. However, the accuracy of vanishing point detection is often affected by noisy or unconverged line segments detected by the line detectors. The problem with using line detectors is that they can produce noisy or unconverged line segments, leading to a decrease in the accuracy of vanishing point detection. Therefore, it is important to develop a method to extract accurate vanishing points from noisy line segments. This paper proposes an algorithm to detect vanishing points by projecting line segments to Gaussian sphere. The proposed method …"
+Irawati Nurmala Sari,Image Processing; Image Inpainting; Deep Learning,High-resolution art painting completion using multi-region laplacian fusion,2023,1,,"Irawati Nurmala Sari, Kei Masaoka, Jun’Nosuke Takarabe, Weiwei Du",2023/6/30,https://scholar.google.com/citations?hl=id&user=d4vypz8AAAAJ,"Image completion has made impressive advancements based on deep learning approaches. However, even with advanced deep learning such as Generative Adversarial Networks (GAN), the restored area is not always optimal due to small-scale texture synthesis in high resolution and inferring missing information about image content from distant contexts, resulting in distorted lines and unnatural colors, especially in art painting completion with complicated structures and textures. Although several precious art paintings have been well-preserved by curators in museums, some frequent damages such as scratches, torn-out areas, and holes are still visible and require challenging physical repairs. Therefore, for practical refinement, some researchers convert them into high-resolution digital paintings to generate crisp brush strokes, textures, shapes, and tones by assuming similarities with the original physical ones …"
+Irawati Nurmala Sari,Image Processing; Image Inpainting; Deep Learning,Prompt Conditioned Batik Pattern Generation using LoRA Weighted Diffusion Model with Classifier-Free Guidance,2024,0,IEEE Access,"Daffa Izzuddin, Novanto Yudistira, Candra Dewi, Irawati Nurmala Sari, Dyanningrum Pradhikta",2024/12/27,https://scholar.google.com/citations?hl=id&user=d4vypz8AAAAJ,"Batik, a significant element of Indonesian cultural heritage, is renowned for its intricate patterns and profound philosophical meanings. While preserving traditional batik is crucial, the creation of modern patterns is equally encouraged to keep the art form vibrant and evolving. Current research primarily focuses on batik classification, leaving a gap in the exploration of generative models for batik pattern creation. This paper investigates the application of text-to-image (T2I) generative models to synthesize batik motifs, leveraging latent diffusion models (LDM), Low-Rank Adaptation (LoRA), and classifier-free guidance. Our methodology employed a dataset of 20,000 batik images. Multimodal models such as LLaVA and BLIP were utilized to generate detailed captions for these images. A pretrained LDM was subsequently fine-tuned on its denoising U-Net part, either by naively fine-tuned the entire layer or by employing …"
+Irawati Nurmala Sari,Image Processing; Image Inpainting; Deep Learning,Artistic Outpainting through Adaptive Image-to-Text and Text-to-Image Generation,2024,0,,"Irawati Nurmala Sari, Ryuto Sugahara, Weiwei Du",2024/4/26,https://scholar.google.com/citations?hl=id&user=d4vypz8AAAAJ,"Artistic heritage often confronts the challenges of degradation, particularly in museum environments where valuable art paintings may exhibit missing regions along their borders. This research addresses the urgent need to restore and revitalize damaged art paintings, utilizing advanced computational methods to harmoniously fill these gaps while preserving the original aesthetic. This paper introduces an innovative approach, employing adaptive Image-to-Text and Text-to-Image Generation for the completion of damaged art paintings, referred to as Artistic Outpainting. Our proposed methodology unfolds in a carefully structured three-step process. Commencing with a pixel-wise network, we employ sophisticated image inpainting techniques to restore art paintings with missing border regions, ensuring a detailed reconstruction that seamlessly integrates additional content. This sets the foundation for subsequent …"
+Irawati Nurmala Sari,Image Processing; Image Inpainting; Deep Learning,Weighted Similarity-Confidence Laplacian Synthesis for High-Resolution Art Painting Completion,2024,0,Applied Sciences,"Irawati Nurmala Sari, Weiwei Du",2024/3/12,https://scholar.google.com/citations?hl=id&user=d4vypz8AAAAJ,"Artistic image completion assumes a significant role in the preservation and restoration of invaluable art paintings, marking notable advancements through the adoption of deep learning methodologies. Despite progress, challenges persist, particularly in achieving optimal results for high-resolution paintings. The intricacies of complex structures and textures in art paintings pose difficulties for sophisticated approaches like Generative Adversarial Networks (GANs), leading to issues such as small-scale texture synthesis and the inference of missing information, resulting in distortions in lines and unnatural colors. Simultaneously, patch-based image synthesis, augmented with global optimization on the image pyramid, has evolved to enhance structural coherence and details. However, gradient-based synthesis methods face obstacles related to directionality, inconsistency, and the computational burdens associated with solving the Poisson equation in non-integrable gradient fields. This paper introduces a pioneering approach, integrating Weighted Similarity-Confidence Laplacian Synthesis to comprehensively address these challenges and advance the field of artistic image completion. Experimental results affirm the effectiveness of our approach, offering promising outcomes for the preservation and restoration of art paintings with intricate details and irregular missing regions. The integration of weighted Laplacian synthesis and patch-based completion across multi-regions ensures precise and targeted completion, outperforming existing methods. A comparative analysis underscores our method’s superiority in artifact reduction and minimizing …"