update space

.gitignore

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+venv

app.py

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+# Import library yang diperlukan
+import streamlit as st
+import joblib
+import re
+import string
+import nltk
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+from Sastrawi.Stemmer.StemmerFactory import StemmerFactory
+
+# Download data NLTK
+nltk.download("stopwords")
+nltk.download("punkt")
+nltk.download("punkt_tab")
+
+# Load model dan vectorizer
+model = joblib.load("random_forest_model.pkl")
+vectorizer = joblib.load("tfidf_vectorizer.pkl")
+
+# Stemmer Bahasa Indonesia
+factory = StemmerFactory()
+stemmer = factory.create_stemmer()
+
+
+# Fungsi untuk membersihkan teks dari karakter yang tidak diperlukan
+def delete_unused_char(text):
+    text = re.sub(r"@[A-Za-z0-9]+", "", text)  # menghapus mention
+    text = re.sub(r"#[A-Za-z0-9]+", "", text)  # menghapus hashtag
+    text = re.sub(r"RT[\s]", "", text)  # menghapus RT
+    text = re.sub(r"http\S+", "", text)  # menghapus link
+    text = re.sub(r"[0-9]+", "", text)  # menghapus angka
+    text = re.sub(r"[^\w\s]", "", text)  # menghapus karakter selain huruf dan angka
+    text = text.replace("\n", " ")  # mengganti baris baru dengan spasi
+    text = text.translate(
+        str.maketrans("", "", string.punctuation)
+    )  # menghapus semua tanda baca
+    text = text.strip(" ")  # menghapus karakter spasi dari kiri dan kanan teks
+    return text
+
+
+# Fungsi untuk membersihkan teks
+def cleaned_text(text):
+    delete_unused_char(text)
+    # 1. Lowercasing
+    text = text.lower()
+    # 2. Remove punctuation
+    text = text.translate(str.maketrans("", "", string.punctuation))
+    # 3. Remove numbers
+    text = re.sub(r"\d+", "", text)
+    # 4. Tokenization
+    words = word_tokenize(text)
+    # 5. Remove stopwords
+    stop_words = set(stopwords.words("indonesian"))  # Stopwords bahasa Indonesia
+    words = [word for word in words if word not in stop_words]
+    # 6. Ubah kate ke bentu asli dengan Stemmer  Sastrawi
+    words = [stemmer.stem(word) for word in words]
+    return " ".join(words)
+
+
+# Fungsi untuk prediksi sentimen
+def predict_sentiment(text):
+    text = cleaned_text(text)  # Preprocessing sebelum prediksi
+    X = vectorizer.transform([text])  # Ubah teks menjadi vektor
+    prediction = model.predict(X)[0]  # Prediksi sentimen
+    return prediction
+
+
+# Streamlit UI
+st.title("Analisis Sentimen Review BRI Mobile 💳")
+st.write("Masukkan review dan dapatkan prediksi sentimen (Positif, Negatif, Netral)")
+
+# Input review dari pengguna
+user_input = st.text_area("Masukkan review di sini:")
+
+if st.button("Prediksi Sentimen"):
+    if user_input.strip() == "":
+        st.warning("Silakan masukkan teks terlebih dahulu!")
+    else:
+        sentiment = predict_sentiment(user_input)
+        st.success(f"Prediksi Sentimen: **{sentiment}**")
+        # st.write(cleaned_text(user_input))
+
+st.write("Dibuat dengan 💖 oleh Muhammad Farkhan Adhitama")

random_forest_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f1a6a774ffd94d3a66550a24d02862caa27e85c711258d07a127baf00f158833
+size 65195937

requirements.txt

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+streamlit
+scikit-learn
+joblib
+nltk
+numpy
+pandas
+Sastrawi

tfidf_vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:65eabfb11ec6e48408db1619a37d01719ac5781385d4720582e451b6f87aa2d4
+size 8008