"dataset and gap"

experiments/08_sentimen_gap.py

@@ -0,0 +1,290 @@
+import os
+os.environ["TF_USE_LEGACY_KERAS"] = "1"  # Bypass Keras 3 & optree
+
+import re
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from datasets import load_dataset
+
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import (
+    classification_report,
+    confusion_matrix,
+    ConfusionMatrixDisplay
+)
+
+import tensorflow as tf
+import tf_keras as keras
+from tf_keras import layers
+from tf_keras.callbacks import EarlyStopping
+from tf_keras.preprocessing.text import Tokenizer  # type: ignore
+from tf_keras.preprocessing.sequence import pad_sequences
+
+# ============================================================
+# KONFIGURASI DIREKTORI PENYIMPANAN
+# ============================================================
+
+VOCAB_SIZE = 10000
+MAX_LEN = 60
+EMBED_DIM = 64
+
+EPOCHS = 20
+BATCH_SIZE = 64
+RANDOM_STATE = 42
+
+FIGURES_DIR = "results/figures"
+REPORTS_DIR = "results/reports"
+MODELS_DIR = "results/models"
+
+os.makedirs(FIGURES_DIR, exist_ok=True)
+os.makedirs(REPORTS_DIR, exist_ok=True)
+os.makedirs(MODELS_DIR, exist_ok=True)
+
+
+# ============================================================
+# SEL 7 — LOAD DATASET TOKOPEDIA
+# ============================================================
+
+print("=" * 60)
+print("SEL 7 — LOAD DATASET")
+print("=" * 60)
+
+dataset = load_dataset(
+    "farhamu/tokopedia-product-reviews-2019"
+)
+
+df = dataset["train"].to_pandas()
+
+# Buang rating 3
+df = df[df["rating"] != 3].copy()
+
+# Rating 4-5 = positif (1), Rating 1-2 = negatif (0)
+df["label"] = (df["rating"] >= 4).astype(int)
+
+# Rename text menjadi teks
+df = df.rename(
+    columns={"text": "teks"}
+)[["teks", "label"]].reset_index(drop=True)
+
+print(df.head(5))
+print(f"\nShape : {df.shape}")
+print(f"Kolom : {df.columns.tolist()}")
+print(f"\nDistribusi label:\n{df['label'].value_counts().sort_index()}")
+print(f"\nNull : {df.isnull().sum().sum()}")
+
+
+# ============================================================
+# DISTRIBUSI LABEL
+# ============================================================
+
+label_counts = df["label"].value_counts().sort_index()
+
+plt.figure(figsize=(7, 5))
+plt.bar(
+    ["Negatif", "Positif"],
+    [label_counts.get(0, 0), label_counts.get(1, 0)]
+)
+plt.title("Distribusi Label Sentimen Tokopedia")
+plt.xlabel("Sentimen")
+plt.ylabel("Jumlah Review")
+plt.tight_layout()
+
+plt.savefig(f"{FIGURES_DIR}/distribusi_label_sentimen.png")
+plt.close()
+
+
+# ============================================================
+# CEK PANJANG TEKS
+# ============================================================
+
+df["panjang"] = df["teks"].apply(lambda x: len(str(x).split()))
+print(f"\nPanjang teks:\n{df['panjang'].describe()}")
+
+
+# ============================================================
+# SEL 8 — TEXT PREPROCESSING
+# ============================================================
+
+print("\n" + "=" * 60)
+print("SEL 8 — TEXT PREPROCESSING")
+print("=" * 60)
+
+def clean_text(teks):
+    teks = str(teks).lower()
+    teks = re.sub(r"[^a-z\s]", "", teks)
+    teks = re.sub(r"\s+", " ", teks).strip()
+    return teks
+
+df["teks_bersih"] = df["teks"].apply(clean_text)
+
+# Train/Test Split
+X = np.array(df["teks_bersih"].tolist())
+y = np.array(df["label"].tolist(), dtype=int)
+
+X_tr, X_te, y_tr, y_te = train_test_split(
+    X, y,
+    test_size=0.2,
+    random_state=RANDOM_STATE,
+    stratify=y
+)
+
+print(f"Training data : {len(X_tr)}")
+print(f"Testing data  : {len(X_te)}")
+
+# Tokenizer
+tokenizer = Tokenizer(num_words=VOCAB_SIZE, oov_token="<OOV>")
+tokenizer.fit_on_texts(X_tr)
+
+# Text -> Integer Sequence
+X_tr_seq = tokenizer.texts_to_sequences(X_tr)
+X_te_seq = tokenizer.texts_to_sequences(X_te)
+
+# Padding
+X_tr_pad = pad_sequences(X_tr_seq, maxlen=MAX_LEN, padding="post")
+X_te_pad = pad_sequences(X_te_seq, maxlen=MAX_LEN, padding="post")
+
+print(f"\nX_tr_pad shape : {X_tr_pad.shape}")
+print(f"X_te_pad shape : {X_te_pad.shape}")
+print(f"Vocab size     : {len(tokenizer.word_index)}")
+
+
+# ============================================================
+# SEL 9 — MODEL GAP
+# ============================================================
+
+print("\n" + "=" * 60)
+print("SEL 9 — MODEL GAP")
+print("=" * 60)
+
+sent_model = keras.Sequential([
+    layers.Embedding(input_dim=VOCAB_SIZE, output_dim=EMBED_DIM, input_length=MAX_LEN),
+    layers.GlobalAveragePooling1D(),
+    layers.Dense(64, activation="relu"),
+    layers.Dropout(0.4),
+    layers.Dense(1, activation="sigmoid")
+], name="Sentimen_GAP")
+
+sent_model.compile(
+    optimizer="adam",
+    loss="binary_crossentropy",
+    metrics=["accuracy"]
+)
+
+sent_model.summary()
+
+
+# ============================================================
+# TRAINING
+# ============================================================
+
+print("\n" + "=" * 60)
+print("TRAINING MODEL GAP")
+print("=" * 60)
+
+early_stop = EarlyStopping(
+    monitor="val_loss",
+    patience=4,
+    restore_best_weights=True
+)
+
+history_sent = sent_model.fit(
+    X_tr_pad, y_tr,
+    epochs=EPOCHS,
+    batch_size=BATCH_SIZE,
+    validation_split=0.15,
+    callbacks=[early_stop],
+    verbose=1
+)
+
+
+# ============================================================
+# TEST ACCURACY & HISTORY PLOTS
+# ============================================================
+
+loss_s, acc_s = sent_model.evaluate(X_te_pad, y_te, verbose=0)
+print(f"\nSentimen GAP → Test accuracy: {acc_s:.4f}")
+
+# Plot Accuracy & Loss
+plt.figure(figsize=(8, 5))
+plt.plot(history_sent.history["accuracy"], label="Training Accuracy")
+plt.plot(history_sent.history["val_accuracy"], label="Validation Accuracy")
+plt.title("Training vs Validation Accuracy — GAP")
+plt.xlabel("Epoch")
+plt.ylabel("Accuracy")
+plt.legend()
+plt.tight_layout()
+plt.savefig(f"{FIGURES_DIR}/histori_gap_sentimen.png")
+plt.close()
+
+
+# ============================================================
+# SEL 11 — EVALUATION
+# ============================================================
+
+print("\n" + "=" * 60)
+print("EVALUATION")
+print("=" * 60)
+
+y_pred_prob = sent_model.predict(X_te_pad, verbose=0)
+y_pred = (y_pred_prob > 0.5).astype("int").flatten()
+
+report = classification_report(
+    y_te, y_pred,
+    target_names=["Negatif (0)", "Positif (1)"]
+)
+
+print("\nClassification Report:")
+print(report)
+
+# Simpan Classification Report Txt
+with open(f"{REPORTS_DIR}/classification_report_gap_sentimen.txt", "w", encoding="utf-8") as f:
+    f.write(report)
+
+# Confusion Matrix Plot
+cm = confusion_matrix(y_te, y_pred)
+disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=["Negatif", "Positif"])
+fig, ax = plt.subplots(figsize=(6, 6))
+disp.plot(ax=ax, cmap="Blues")
+plt.title("Confusion Matrix — Model Sentimen GAP")
+plt.tight_layout()
+plt.savefig(f"{FIGURES_DIR}/confusion_matrix_gap_sentimen.png")
+plt.close()
+
+
+# ============================================================
+# SEL 12 — INFERENCE (PREDIKSI 5 ULASAN)
+# ============================================================
+
+def prediksi_sentimen(teks, model, tokenizer, maxlen):
+    teks_bersih = clean_text(teks)
+    seq = tokenizer.texts_to_sequences([teks_bersih])
+    padded = pad_sequences(seq, maxlen=maxlen, padding="post")
+    prob = model.predict(padded, verbose=0)[0][0]
+    label = "Positif" if prob > 0.5 else "Negatif"
+    return label, prob
+
+print("\n" + "=" * 60)
+print("PREDIKSI 5 ULASAN")
+print("=" * 60)
+
+ulasan_uji = [
+    "Produk berkualitas tinggi, pengiriman cepat, packing aman!",
+    "Kecewa banget, barang datang rusak dan customer service tidak responsif",
+    "Lumayan sih, tapi harganya kemahalan untuk kualitas segitu",
+    "Tidak sesuai deskripsi sama sekali, mau return tapi ribet",
+    "Barang bagus, sesuai dengan foto dan penjual sangat ramah"
+]
+
+for i, ulasan in enumerate(ulasan_uji, start=1):
+    label, prob = prediksi_sentimen(ulasan, sent_model, tokenizer, MAX_LEN)
+    print(f"\n{i}. {ulasan}\n   Prediksi : {label}\n   Confidence: {prob:.3f}")
+
+
+# ============================================================
+# SIMPAN MODEL
+# ============================================================
+
+sent_model.save(f"{MODELS_DIR}/model_gap_sentimen.keras")
+print("\nModel GAP berhasil disimpan di folder results/models/")

requirements.txt

@@ -7,7 +7,8 @@ scikit-learn>=1.3.0
 # Scientific Computing & Data Manipulation
 numpy>=1.24.0
 pandas>=2.0.0
+datasets>=2.0.0
 
 # Data Visualization
 matplotlib>=3.7.0
-seaborn>=0.12.0
+seaborn>=0.12.0

results/reports/classification_report_gap_sentimen.txt

@@ -0,0 +1,8 @@
+              precision    recall  f1-score   support
+
+ Negatif (0)       0.58      0.34      0.42       185
+ Positif (1)       0.98      0.99      0.99      7572
+
+    accuracy                           0.98      7757
+   macro avg       0.78      0.66      0.71      7757
+weighted avg       0.97      0.98      0.98      7757