Datasets:

gibranlynardi
/

dataset_aplikasi_merek_pdki

Tasks:

Tabular Classification

Feature Extraction

Tags:

legal

finance

License:

Dataset card Files Files and versions

xet

Community

Dataset Viewer

The dataset viewer is not available for this dataset.

Cannot get the config names for the dataset.

Error code:   ConfigNamesError
Exception:    ReadTimeout
Message:      (ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: 815bc65e-f60e-46e0-a739-40bf7c9d880f)')
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1031, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 996, in dataset_module_factory
                  return HubDatasetModuleFactory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 591, in get_module
                  standalone_yaml_path = cached_path(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 167, in cached_path
                  resolved_path = huggingface_hub.HfFileSystem(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_file_system.py", line 198, in resolve_path
                  repo_and_revision_exist, err = self._repo_and_revision_exist(repo_type, repo_id, revision)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_file_system.py", line 125, in _repo_and_revision_exist
                  self._api.repo_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
                  return fn(*args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_api.py", line 2816, in repo_info
                  return method(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
                  return fn(*args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_api.py", line 2673, in dataset_info
                  r = get_session().get(path, headers=headers, timeout=timeout, params=params)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/sessions.py", line 602, in get
                  return self.request("GET", url, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/sessions.py", line 589, in request
                  resp = self.send(prep, **send_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/sessions.py", line 703, in send
                  r = adapter.send(request, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_http.py", line 96, in send
                  return super().send(request, *args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/adapters.py", line 635, in send
                  raise ReadTimeout(e, request=request)
              requests.exceptions.ReadTimeout: (ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: 815bc65e-f60e-46e0-a739-40bf7c9d880f)')

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📁 Data Aplikasi Merek Dagang Indonesia (PDKI)

✅ Overview

Dataset ini merupakan kumpulan aplikasi merek dagang di Indonesia, diperoleh dari PD KI (Pangkalan Data Kekayaan Intelektual), DJKI, periode 1988–2024. Metadata mencakup:

Brand name
Nama pemilik
Tahun permohonan
Kode & deskripsi NICE class
Status aplikasi (terdaftar / ditolak)
Logo (jika tersedia)

Total sample: 337.334, dengan 87.424 sampel memiliki logo. Data ini digunakan untuk pelatihan dan evaluasi KeloraAI :contentReference[oaicite:1]{index=1}.

🧭 Use Cases

Tabular classification: memprediksi status aplikasi (registered vs rejected).
Feature extraction: mengkalkulasi embedding teks dan visual dari nama merek dan logo.

Digunakan oleh:

Legal-tech untuk deteksi dini aplikasi bermasalah
Peneliti NLP/CV untuk analisis similarity teks & gambar
Konsultan IP dan pelaku UKM untuk screening merek sebelum daftar

🛠️ Data Structure

Brand (str): Nama merek
Nama Pemilik (str): Pemilik aplikasi
Tahun Permohonan (int): Tahun permohonan (1988–2024)
kode_kelas_ (str/int): Kode NICE 1–45
Deskripsi Kelas (str): Deskripsi barang/jasa
Status_Encoded (str): "Didaftar" atau "Ditolak"
Logo File (file path / base64): Opsional logo merek (~87.4k samples)

Detail embedding & feature-engineering dapat dilihat di notebook notebook-bebas-dahh-pipelines-and-training.ipynb pada https://github.com/bebasdahh/BebasDahh_Datathon/blob/main/notebook-bebas-dahh-pipelines-and-training.ipynb

📦 File Content

Typical files include:

Validation_new.csv / Train_new.csv / Test_new.csv: tabular data utama
Validation_sparse.parquet / Train_sparse.parquet / Test_sparse.parquet: tabular data feature engineered dengan embedding sparse
Validation_dense.parquet / Train_dense.parquet / Test_dense.parquet: tabular data feature engineered dengan embedding dense
logos/: direktori berisi file logo

📊 Statistics

Total sampel: 337.334
Logo tersedia: 87.424 (~26%)
Kelas NICE: 1–45 (produk & jasa) :contentReference[oaicite:2]{index=2}
Tahun range: 1988–2024
Imbalance label: lebih banyak status "Didaftar" dibanding "Ditolak"

🎯 Motivation & Origin

Dataset ini dikumpulkan secara scrapping dari situs resmi DJKI PDKI (sistem publik yang memungkinkan pemantauan status KI sejak 2015) :contentReference[oaicite:3]{index=3}. Digunakan dalam paper KeloraAI, yang memperlihatkan bahwa embedding visual (DINOv2) menghasilkan F1-score hingga 0.9072 untuk memprediksi status aplikasi merek :contentReference[oaicite:4]{index=4}.

⚠️ Bias & Limitations

Historical bias: hanya mencakup aplikasi hingga 2024; tren terbaru belum termasuk.
Visual coverage: ~26% memiliki logo; model berbasis visual hanya bisa diaplikasikan pada subset ini.
Class imbalance: lebih banyak data "Didaftar" → evaluasi F1 pada kelas minoritas penting.
Legalese: hanya status, tidak termasuk detail alasan penolakan (tantangan data).

📄 License & Citation

Lisensi: Apache‑2.0

@article{lynardi2024keloraai,
  title={KeloraAI: Sistem Open-source Prediksi Keberhasilan Aplikasi Merek Dagang menggunakan Embedding Based Semantic Retreival dan Visual Search Classification},
  author={Lynardi, G. T. R. P. and Syafitra, D. and Firdaus, H. A.},
  journal={Fakultas Ilmu Komputer, Universitas Indonesia},
  year={2024}
}

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Models trained or fine-tuned on gibranlynardi/dataset_aplikasi_merek_pdki

gibranlynardi/catboost_prediksi_aplikasi_merek

Tabular Classification • Updated Jul 13, 2025