Upload 4 files

app.py

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+# app.py
+import streamlit as st
+import pickle
+import re
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+import nltk
+
+
+nltk.download('punkt')
+nltk.download('stopwords')
+
+# Load model dan tools
+with open("model_sentiment.pkl", "rb") as f:
+    model = pickle.load(f)
+
+with open("vectorizer.pkl", "rb") as f:
+    vectorizer = pickle.load(f)
+
+with open("label_encoder.pkl", "rb") as f:
+    le = pickle.load(f)
+
+stop_words = set(stopwords.words('indonesian'))
+
+# Preprocessing function
+def preprocess(text):
+    text = text.lower()
+    text = re.sub(r'[^a-zA-Z\s]', '', text)
+    tokens = word_tokenize(text)
+    tokens = [t for t in tokens if t not in stop_words]
+    return ' '.join(tokens)
+
+# UI
+st.title("🧠 Sentiment Analysis Komentar Bahasa Indonesia")
+st.markdown("Masukkan komentar di bawah ini:")
+
+user_input = st.text_area("💬 Komentar")
+
+if st.button("Prediksi Sentimen"):
+    if user_input.strip() == "":
+        st.warning("Komentar tidak boleh kosong!")
+    else:
+        cleaned = preprocess(user_input)
+        vec = vectorizer.transform([cleaned])
+        pred = model.predict(vec)
+        label = le.inverse_transform(pred)[0]
+        
+        if label == "positif":
+            st.success(f"Hasil: {label.capitalize()} 👍")
+        elif label == "negatif":
+            st.error(f"Hasil: {label.capitalize()} 👎")
+        else:
+            st.info(f"Hasil: {label.capitalize()} 😐")

label_encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b982ce52466d824af64291d28cd1be7a5a88dfa3cc59936b87106f71057e0da5
+size 271

model_sentiment.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ac424f45fc03607c49b71742c31eadf1169dfd9eeb92d2af84f7dbdbbbf8ac7e
+size 53572

vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:de3c9cf2898b6cee95d9041515b68b4427343bd921af825811ab5ca5b47a4695
+size 30783