refactor: re-structure folder

2026-09-21 09:04:44 +07:00
parent 7492704c8e
commit 5ac0a227c4
26 changed files with 911 additions and 944 deletions
@@ -0,0 +1,99 @@
+import sys
+import os
+import time
+
+# Tambahkan root directory ke path agar dapat import dari src
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+
+from src.data_loader import load_mnist_for_mlp
+from src.models import create_mlp_model
+
+
+def main():
+    print("=" * 60)
+    print("TUGAS 1: MEMUAT DATASET MNIST & EKSPERIMEN MLP")
+    print("=" * 60)
+
+    # 1. Load Data
+    (x_train, y_train), (x_test, y_test) = load_mnist_for_mlp()
+    print(f"Data training : {x_train.shape}")
+    print(f"Data testing  : {x_test.shape}")
+
+    # 2. Konfigurasi 3 Model MLP
+    models_config = {
+        "Model A - 1 Hidden Layer": [64],
+        "Model B - 2 Hidden Layer": [128, 64],
+        "Model C - 3 Hidden Layer": [256, 128, 64]
+    }
+
+    results = []
+    EPOCHS = 10
+    BATCH_SIZE = 128
+
+    # 3. Training & Evaluasi
+    for model_name, architecture in models_config.items():
+        print("\n" + "=" * 60)
+        print(model_name)
+        print("=" * 60)
+
+        model = create_mlp_model(architecture)
+        model.summary()
+
+        print("\nMemulai training...")
+        start_time = time.time()
+
+        model.fit(
+            x_train,
+            y_train,
+            epochs=EPOCHS,
+            batch_size=BATCH_SIZE,
+            validation_split=0.1,
+            verbose=1
+        )
+
+        total_time = time.time() - start_time
+        average_time = total_time / EPOCHS
+
+        test_loss, test_accuracy = model.evaluate(x_test, y_test, verbose=0)
+        total_parameters = model.count_params()
+
+        results.append({
+            "model": model_name,
+            "parameters": total_parameters,
+            "accuracy": test_accuracy * 100,
+            "time_per_epoch": average_time
+        })
+
+        print("\nHasil:")
+        print(f"Test Accuracy     : {test_accuracy * 100:.2f}%")
+        print(f"Jumlah Parameter  : {total_parameters:,}")
+        print(f"Waktu/Epoch       : {average_time:.2f} detik")
+
+    # 4. Ringkasan Perbandingan
+    print("\n\n" + "=" * 80)
+    print("HASIL PERBANDINGAN 3 MODEL MLP")
+    print("=" * 80)
+    print(f"{'Model':<30}{'Parameter':>15}{'Accuracy':>15}{'Time/Epoch':>15}")
+    print("-" * 80)
+
+    for res in results:
+        print(
+            f"{res['model']:<30}"
+            f"{res['parameters']:>15,}"
+            f"{res['accuracy']:>14.2f}%"
+            f"{res['time_per_epoch']:>14.2f}s"
+        )
+
+    best_model = max(results, key=lambda x: x["accuracy"])
+    print("\n" + "=" * 60)
+    print("MODEL DENGAN TEST ACCURACY TERTINGGI")
+    print("=" * 60)
+    print(f"Model               : {best_model['model']}")
+    print(f"Test Accuracy       : {best_model['accuracy']:.2f}%")
+    print(f"Jumlah Parameter    : {best_model['parameters']:,}")
+    print(f"Waktu Training/Epoch: {best_model['time_per_epoch']:.2f} detik")
+
+
+if __name__ == "__main__":
+    main()
+