diff --git a/task-2-Regulation/model_c_regularization.py b/task-2-Regulation/model_c_regularization.py
new file mode 100644
index 0000000..f5afa69
--- /dev/null
+++ b/task-2-Regulation/model_c_regularization.py
@@ -0,0 +1,123 @@
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+import matplotlib.pyplot as plt
+
+# 1. LOAD & PREPROCESS MNIST DATASET
+(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
+
+x_train = x_train.astype("float32") / 255.0
+x_test = x_test.astype("float32") / 255.0
+x_train = x_train.reshape(-1, 784)
+x_test = x_test.reshape(-1, 784)
+
+# Model C Architecture from Task 1
+BEST_ARCHITECTURE = [256, 128, 64]
+EPOCHS = 20  # 20 epochs gives enough room to observe overfitting
+BATCH_SIZE = 128
+
+
+# ============================================================
+# MODEL 1: WITHOUT REGULARIZATION (Baseline Model C from Task 1)
+# ============================================================
+model_base = keras.Sequential([
+    layers.Input(shape=(784,)),
+    layers.Dense(256, activation="relu"),
+    layers.Dense(128, activation="relu"),
+    layers.Dense(64, activation="relu"),
+    layers.Dense(10, activation="softmax")
+])
+
+model_base.compile(
+    optimizer="adam",
+    loss="sparse_categorical_crossentropy",
+    metrics=["accuracy"]
+)
+
+print("=" * 60)
+print("TRAINING BASELINE MODEL (WITHOUT REGULARIZATION)")
+print("=" * 60)
+
+history_base = model_base.fit(
+    x_train, y_train,
+    epochs=EPOCHS,
+    batch_size=BATCH_SIZE,
+    validation_split=0.1,
+    verbose=1
+)
+
+
+# ============================================================
+# MODEL 2: WITH REGULARIZATION (Batch Normalization + Dropout 0.3)
+# ============================================================
+model_reg = keras.Sequential([
+    layers.Input(shape=(784,)),
+    
+    layers.Dense(256),
+    layers.BatchNormalization(),
+    layers.Activation("relu"),
+    layers.Dropout(0.3),
+    
+    layers.Dense(128),
+    layers.BatchNormalization(),
+    layers.Activation("relu"),
+    layers.Dropout(0.3),
+    
+    layers.Dense(64),
+    layers.BatchNormalization(),
+    layers.Activation("relu"),
+    layers.Dropout(0.3),
+    
+    layers.Dense(10, activation="softmax")
+])
+
+model_reg.compile(
+    optimizer="adam",
+    loss="sparse_categorical_crossentropy",
+    metrics=["accuracy"]
+)
+
+print("\n" + "=" * 60)
+print("TRAINING REGULARIZED MODEL (WITH BATCHNORM + DROPOUT)")
+print("=" * 60)
+
+history_reg = model_reg.fit(
+    x_train, y_train,
+    epochs=EPOCHS,
+    batch_size=BATCH_SIZE,
+    validation_split=0.1,
+    verbose=1
+)
+
+# Optional: Save the trained regularized model to disk
+model_reg.save("model_c_regularized.keras")
+print("\n[INFO] Regularized model saved as 'model_c_regularized.keras' in your project directory.")
+
+
+# ============================================================
+# PLOTTING LOSS CURVES FOR COMPARISON
+# ============================================================
+plt.figure(figsize=(14, 5))
+
+# Plot 1: Baseline (Without Regularization)
+plt.subplot(1, 2, 1)
+plt.plot(history_base.history['loss'], label='Training Loss', color='blue')
+plt.plot(history_base.history['val_loss'], label='Validation Loss', color='orange', linestyle='--')
+plt.title('Model C Without Regularization')
+plt.xlabel('Epoch')
+plt.ylabel('Loss')
+plt.legend()
+plt.grid(True)
+
+# Plot 2: Regularized (With BatchNorm + Dropout)
+plt.subplot(1, 2, 2)
+plt.plot(history_reg.history['loss'], label='Training Loss', color='blue')
+plt.plot(history_reg.history['val_loss'], label='Validation Loss', color='green', linestyle='--')
+plt.title('Model C With BatchNorm + Dropout(0.3)')
+plt.xlabel('Epoch')
+plt.ylabel('Loss')
+plt.legend()
+plt.grid(True)
+
+plt.tight_layout()
+plt.show()
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diff --git a/task1-models/codingan.py b/task1-models/codingan.py
new file mode 100644
index 0000000..caa0982
--- /dev/null
+++ b/task1-models/codingan.py
@@ -0,0 +1,224 @@
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+import time
+
+
+# ============================================================
+# 1. LOAD DATASET MNIST
+# ============================================================
+
+print("=" * 60)
+print("MEMUAT DATASET MNIST")
+print("=" * 60)
+
+(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
+
+print(f"Data training : {x_train.shape}")
+print(f"Data testing  : {x_test.shape}")
+
+
+# ============================================================
+# 2. PREPROCESSING
+# ============================================================
+
+# Normalisasi nilai pixel dari 0-255 menjadi 0-1
+x_train = x_train.astype("float32") / 255.0
+x_test = x_test.astype("float32") / 255.0
+
+# Mengubah gambar 28x28 menjadi 784 fitur
+x_train = x_train.reshape(-1, 784)
+x_test = x_test.reshape(-1, 784)
+
+print(f"Training setelah preprocessing: {x_train.shape}")
+print(f"Testing setelah preprocessing : {x_test.shape}")
+
+
+# ============================================================
+# 3. FUNGSI MEMBUAT MODEL
+# ============================================================
+
+def create_model(hidden_layers):
+
+    model = keras.Sequential()
+
+    # Input layer
+    model.add(layers.Input(shape=(784,)))
+
+    # Hidden layers
+    for neurons in hidden_layers:
+        model.add(
+            layers.Dense(
+                neurons,
+                activation="relu"
+            )
+        )
+
+    # Output layer
+    model.add(
+        layers.Dense(
+            10,
+            activation="softmax"
+        )
+    )
+
+    model.compile(
+        optimizer="adam",
+        loss="sparse_categorical_crossentropy",
+        metrics=["accuracy"]
+    )
+
+    return model
+
+
+# ============================================================
+# 4. KONFIGURASI 3 MODEL
+# ============================================================
+
+models_config = {
+    "Model A - 1 Hidden Layer": [64],
+    "Model B - 2 Hidden Layer": [128, 64],
+    "Model C - 3 Hidden Layer": [256, 128, 64]
+}
+
+
+# ============================================================
+# 5. TRAINING DAN EVALUASI
+# ============================================================
+
+results = []
+
+EPOCHS = 10
+BATCH_SIZE = 128
+
+for model_name, architecture in models_config.items():
+
+    print("\n")
+    print("=" * 60)
+    print(model_name)
+    print("=" * 60)
+
+    # Membuat model
+    model = create_model(architecture)
+
+    # Menampilkan arsitektur
+    model.summary()
+
+    # Training
+    print("\nMemulai training...")
+
+    start_time = time.time()
+
+    history = model.fit(
+        x_train,
+        y_train,
+        epochs=EPOCHS,
+        batch_size=BATCH_SIZE,
+        validation_split=0.1,
+        verbose=1
+    )
+
+    end_time = time.time()
+
+    # Total waktu training
+    total_time = end_time - start_time
+
+    # Rata-rata waktu per epoch
+    average_time = total_time / EPOCHS
+
+    # Evaluasi menggunakan test data
+    test_loss, test_accuracy = model.evaluate(
+        x_test,
+        y_test,
+        verbose=0
+    )
+
+    # Jumlah parameter
+    total_parameters = model.count_params()
+
+    # Menyimpan hasil
+    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")
+
+
+# ============================================================
+# 6. HASIL PERBANDINGAN
+# ============================================================
+
+print("\n\n")
+print("=" * 80)
+print("HASIL PERBANDINGAN 3 MODEL MLP")
+print("=" * 80)
+
+print(
+    f"{'Model':<30}"
+    f"{'Parameter':>15}"
+    f"{'Accuracy':>15}"
+    f"{'Time/Epoch':>15}"
+)
+
+print("-" * 80)
+
+for result in results:
+
+    print(
+        f"{result['model']:<30}"
+        f"{result['parameters']:>15,}"
+        f"{result['accuracy']:>14.2f}%"
+        f"{result['time_per_epoch']:>14.2f}s"
+    )
+
+
+# ============================================================
+# 7. MENENTUKAN MODEL DENGAN ACCURACY TERTINGGI
+# ============================================================
+
+best_model = max(
+    results,
+    key=lambda x: x["accuracy"]
+)
+
+print("\n")
+print("=" * 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")
+
+
+# ============================================================
+# 8. KESIMPULAN SINGKAT
+# ============================================================
+
+print("\n")
+print("=" * 60)
+print("KESIMPULAN")
+print("=" * 60)
+
+print(
+    "Model yang lebih dalam memiliki jumlah parameter "
+    "yang lebih banyak dan umumnya membutuhkan waktu "
+    "training yang lebih besar."
+)
+
+print(
+    "Namun, model yang lebih dalam tidak selalu menghasilkan "
+    "peningkatan test accuracy yang signifikan."
+)
+
+print(
+    "Pemilihan arsitektur sebaiknya mempertimbangkan "
+    "accuracy, jumlah parameter, dan waktu training."
+)
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