82 lines
3.1 KiB
Python
82 lines
3.1 KiB
Python
# 3 evaluasi hasil model
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import joblib
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import matplotlib.pyplot as plt
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import seaborn as sns
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from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
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import os
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print("📊 [TAHAP 3] Evaluasi Hasil Model...")
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# --- PENYESUAIAN FOLDER ---
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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FOLDER_OUTPUT = os.path.join(BASE_DIR, 'output')
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FOLDER_MODELS = os.path.join(BASE_DIR, 'models')
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FOLDER_IMAGES = os.path.join(BASE_DIR, 'images')
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# 0. CEK KEAMANAN
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file_xtest = f'{FOLDER_OUTPUT}/X_test.pkl'
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file_svm = f'{FOLDER_MODELS}/model_svm.pkl'
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if not os.path.exists(file_xtest):
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raise FileNotFoundError(f"❌ File '{file_xtest}' tidak ditemukan! Pastikan Tahap 1 sudah dijalankan.")
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if not os.path.exists(file_svm):
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raise FileNotFoundError(f"❌ File '{file_svm}' tidak ditemukan! Pastikan Tahap 2 sudah selesai 100%.")
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# Bikin folder images otomatis di dalam virtualEnvironment
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os.makedirs(FOLDER_IMAGES, exist_ok=True)
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# 1. Ambil Data Uji
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print(" - Memuat data uji...")
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X_test = joblib.load(file_xtest)
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y_test = joblib.load(f'{FOLDER_OUTPUT}/y_test.pkl')
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# 2. Daftar Model
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print(" - Memuat model-model AI...")
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daftar_model = {
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"SVM": joblib.load(file_svm),
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"KNN": joblib.load(f'{FOLDER_MODELS}/model_knn.pkl'),
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"Ensemble": joblib.load(f'{FOLDER_MODELS}/model_ensemble.pkl')
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}
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# 3. Loop Evaluasi
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for nama, model in daftar_model.items():
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print(f"\n==========================================")
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print(f"--- Evaluasi Model: {nama} ---")
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print(f"==========================================")
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# Lakukan Prediksi
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y_pred = model.predict(X_test)
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# Hitung Akurasi
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acc = accuracy_score(y_test, y_pred)
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print(f"🎯 Akurasi {nama}: {acc*100:.2f}%\n")
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# Laporan Lengkap (Precision, Recall, F1-Score)
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print("📋 Laporan Klasifikasi:")
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print(classification_report(y_test, y_pred))
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# Bikin Grafik Confusion Matrix
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cm = confusion_matrix(y_test, y_pred)
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plt.figure(figsize=(7, 5))
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sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
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annot_kws={"size": 14}) # Angka di dalam kotak diperbesar
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# Hiasan Grafik (Lebih rapi untuk masuk ke buku Skripsi)
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plt.title(f'Confusion Matrix - {nama}\n(Akurasi: {acc*100:.2f}%)', fontsize=14, fontweight='bold', pad=15)
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plt.xlabel('Prediksi Mesin', fontsize=12, fontweight='bold')
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plt.ylabel('Label Asli', fontsize=12, fontweight='bold')
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# Tambahan Keterangan Sumbu X dan Y biar dosen mudah baca
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plt.xticks(ticks=[0.5, 1.5, 2.5], labels=['0 (Netral)', '1 (rasional negatif)', '2 (cacimaki/intoleransi)'])
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plt.yticks(ticks=[0.5, 1.5, 2.5], labels=['0 (Netral)', '1 (rasional negatif)', '2 (cacimaki/intoleransi)'], rotation=0)
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# Simpan Gambar dengan resolusi tinggi (dpi=300)
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nama_file = f'{FOLDER_IMAGES}/cm_{nama}.png'
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plt.savefig(nama_file, bbox_inches='tight', dpi=300)
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print(f"🖼️ Grafik Confusion Matrix tersimpan: {nama_file}")
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# Tutup plot supaya tidak numpuk di memori
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plt.close()
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print("\n🎉 SEMUA TAHAPAN SELESAI!")
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print(f"Cek folder '{FOLDER_IMAGES}' untuk melihat gambar Confusion Matrix-nya ya.") |