refactor: restrukturisasi folder eksperimen ke tasks modular

README.md

@@ -6,32 +6,48 @@ Repository ini berisi implementasi tugas dan eksperimen Deep Learning, mencakup
 
 ## 📁 Struktur Folder Proyek
 
-Struktur direktori telah dirapikan secara modular sebagai berikut:
+Struktur direktori dirapikan secara modular dan terkelompok berdasarkan topik tugas:
 
 ```text
 kelompok3-deeplearning/
 ├── .gitignore                      # Konfigurasi file & folder yang diabaikan git
 ├── requirements.txt                # Daftar pustaka / dependensi Python
 ├── README.md                       # Dokumentasi utama proyek
-├── docs/                           # Dokumentasi dan analisis tambahan
+├── docs/                           # Dokumentasi dan analisis laporan
-│   └── model_selection.md          # Analisis justifikasi pemilihan model (Tugas 3)
+│   ├── laporan_akhir.md
-├── src/                            # Modul reusable / fungsi pembantu
+│   ├── PRESENTATION.md
+│   ├── model_selection.md
+│   └── analisis_evaluasi_model_sentimen_gap.md
+├── src/                            # Modul reusable / fungsi pembantu bersama
 │   ├── __init__.py
 │   ├── data_loader.py              # Loader & preprocessor dataset MNIST (MLP & CNN)
 │   ├── models.py                   # Definisi arsitektur model (MLP, Regularized MLP, CNN)
 │   └── utils.py                    # Callback timer, manajemen folder, plotting helper
-├── experiments/                    # Skrip eksperimen & tugas utama
+├── tasks/                          # Skrip tugas yang dikelompokkan berdasarkan topik
-│   ├── 01_mlp_comparison.py        # Tugas 1: Perbandingan 3 arsitektur MLP
+│   ├── mlp/                        # Topik 1: Multi-Layer Perceptron (MNIST)
-│   ├── 02_regularization.py        # Tugas 2: Eksperimen Regularisasi BatchNorm + Dropout
+│   │   ├── 01_mlp_comparison.py    # Eksperimen perbandingan 3 arsitektur MLP
-│   ├── 03_model_selection.py       # Tugas 3: Implementasi contoh arsitektur model
+│   │   ├── 02_regularization.py    # Eksperimen regularisasi (BatchNorm + Dropout)
-│   └── 04_cnn_mnist.py             # Tugas 4: Implementasi CNN MNIST & perbandingan vs MLP
+│   │   └── 03_model_selection.py   # Studi kasus pemilihan arsitektur model
-└── results/                        # Output dari eksperimen
+│   ├── cnn/                        # Topik 2: Convolutional Neural Network (MNIST)
-    ├── figures/                    # Plot visualisasi (akurasi, loss curve, bar chart)
+│   │   ├── 04_cnn_mnist.py         # Baseline CNN vs MLP
-    │   ├── hasil_cnn.png
+│   │   ├── 05_cnn_full_pipeline.py # Pipeline CNN lengkap (Loss/Acc curve, Confusion Matrix)
-    │   └── loss_regularization.png
+│   │   ├── 06_cnn_deeper.py        # Eksplorasi kedalaman CNN (2 blok vs 3 blok)
-    └── models/                     # Bobot model terlatih (.keras)
+│   │   └── 07_cnn_evaluation.py    # Evaluasi mendalam (per-class metrics, confidence, misclassified)
-        ├── model_cnn_mnist.keras
+│   └── nlp_sentiment/              # Topik 3: NLP Analisis Sentimen (Tokopedia)
-        └── model_c_regularized.keras
+│       └── 08_sentimen_gap.py      # Klasifikasi sentimen ulasan Tokopedia (GlobalAveragePooling1D)
+└── results/                        # Output terorganisir per kelompok tugas
+    ├── 01_mlp/
+    │   ├── figures/                # Visualisasi grafik MLP & regularisasi
+    │   ├── models/                 # Model terlatih MLP (.keras)
+    │   └── reports/                # Laporan & ringkasan metrik MLP (.csv)
+    ├── 02_cnn/
+    │   ├── figures/                # Visualisasi grafik, confusion matrix, error analysis CNN
+    │   ├── models/                 # Model terlatih CNN (.keras)
+    │   └── reports/                # Classification report (.txt, .csv) & evaluasi CNN
+    └── 03_nlp_sentiment/
+        ├── figures/                # Distribusi label, kurva training, confusion matrix GAP
+        ├── models/                 # Model terlatih sentiment GAP (.keras)
+        └── reports/                # Classification report sentimen (.txt)
 ```
 
 ---
@@ -56,29 +72,27 @@ python -m venv .venv
 pip install -r requirements.txt
 ```
 
-### 3. Menjalankan Eksperimen
+### 3. Menjalankan Tugas
 
-Semua skrip eksperimen berada di dalam folder `experiments/`:
+#### 🔹 Topik 1: Multi-Layer Perceptron (MLP)
+```bash
+python tasks/mlp/01_mlp_comparison.py
+python tasks/mlp/02_regularization.py
+python tasks/mlp/03_model_selection.py
+```
 
-- **Eksperimen 1: Perbandingan Arsitektur MLP**
+#### 🔹 Topik 2: Convolutional Neural Network (CNN)
-  ```bash
+```bash
-  python experiments/01_mlp_comparison.py
+python tasks/cnn/04_cnn_mnist.py
-  ```
+python tasks/cnn/05_cnn_full_pipeline.py
+python tasks/cnn/06_cnn_deeper.py
+python tasks/cnn/07_cnn_evaluation.py
+```
 
-- **Eksperimen 2: Regularisasi (BatchNorm + Dropout)**
+#### 🔹 Topik 3: NLP Analisis Sentimen
-  ```bash
+```bash
-  python experiments/02_regularization.py
+python tasks/nlp_sentiment/08_sentimen_gap.py
-  ```
+```
-
-- **Eksperimen 3: Contoh Model Selection (LSTM, CNN, NLP, XGBoost)**
-  ```bash
-  python experiments/03_model_selection.py
-  ```
-
-- **Eksperimen 4: CNN MNIST vs MLP**
-  ```bash
-  python experiments/04_cnn_mnist.py
-  ```
 
 ---
 

tasks/cnn/04_cnn_mnist.py

@@ -17,7 +17,7 @@ import numpy as np
 import tensorflow as tf
 
 # Tambahkan root directory ke path agar dapat import dari src
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
 
 from src.data_loader import load_mnist_for_cnn
 from src.models import create_cnn_mnist_model
@@ -105,8 +105,8 @@ def main():
     print(f"Selisih parameter        : {n_params - MLP_PARAMS:+,}")
     print("=" * 62)
 
-    # 6. Simpan grafik ke folder results/figures
+    # 6. Simpan grafik ke folder results/02_cnn/figures
-    results_fig_dir = os.path.join("results", "figures")
+    results_fig_dir = os.path.join("results", "02_cnn", "figures")
     ensure_dir(results_fig_dir)
     fig_path = os.path.join(results_fig_dir, "hasil_cnn.png")
 
@@ -135,8 +135,8 @@ def main():
     plt.savefig(fig_path, dpi=130)
     print(f"\n[INFO] Grafik disimpan di: {fig_path}")
 
-    # 7. Simpan model terlatih ke folder results/models
+    # 7. Simpan model terlatih ke folder results/02_cnn/models
-    results_model_dir = os.path.join("results", "models")
+    results_model_dir = os.path.join("results", "02_cnn", "models")
     ensure_dir(results_model_dir)
     model_path = os.path.join(results_model_dir, "model_cnn_mnist.keras")
     model.save(model_path)

tasks/cnn/05_cnn_full_pipeline.py

@@ -22,7 +22,7 @@ from sklearn.metrics import classification_report, confusion_matrix
 import tensorflow as tf
 
 # Tambahkan root directory ke path
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
 
 from src.data_loader import load_mnist_for_cnn
 from src.models import create_cnn_mnist_model
@@ -87,7 +87,7 @@ def main():
     print(f"  -> Test Loss     : {test_loss:.4f}")
 
     # 5. Visualisasi & Penyimpanan
-    figures_dir = os.path.join("results", "figures")
+    figures_dir = os.path.join("results", "02_cnn", "figures")
     ensure_dir(figures_dir)
 
     # (b) Loss & Accuracy Curve
@@ -185,10 +185,10 @@ def main():
     print("=" * 70 + "\n")
 
     # Simpan bobot terbaru
-    models_dir = os.path.join("results", "models")
+    models_dir = os.path.join("results", "02_cnn", "models")
     ensure_dir(models_dir)
     model.save(os.path.join(models_dir, "model_cnn_mnist.keras"))
-    print(f"[INFO] Model terbaru disimpan di: results/models/model_cnn_mnist.keras")
+    print(f"[INFO] Model terbaru disimpan di: {os.path.join(models_dir, 'model_cnn_mnist.keras')}")
 
 
 if __name__ == "__main__":

tasks/cnn/06_cnn_deeper.py

@@ -21,7 +21,7 @@ import numpy as np
 import tensorflow as tf
 
 # Tambahkan root directory ke path
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
 
 from src.data_loader import load_mnist_for_cnn
 from src.models import create_cnn_mnist_model, create_deep_cnn_mnist_model
@@ -104,7 +104,7 @@ def main():
         histories[model_name] = history
 
     # Simpan model 3 blok
-    models_dir = os.path.join("results", "models")
+    models_dir = os.path.join("results", "02_cnn", "models")
     ensure_dir(models_dir)
     results[1]["model_obj"].save(os.path.join(models_dir, "model_cnn_3blocks.keras"))
 
@@ -128,7 +128,7 @@ def main():
     print("=" * 75)
 
     # Visualisasi
-    figures_dir = os.path.join("results", "figures")
+    figures_dir = os.path.join("results", "02_cnn", "figures")
     ensure_dir(figures_dir)
     fig_path = os.path.join(figures_dir, "perbandingan_kedalaman_cnn.png")
 

tasks/cnn/07_cnn_evaluation.py

@@ -43,7 +43,7 @@ import tensorflow as tf
 from tensorflow import keras
 
 # Tambahkan root directory ke path
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
 
 from src.data_loader import load_mnist_for_cnn
 from src.utils import ensure_dir
@@ -56,9 +56,9 @@ EPOCHS = 10          # epoch training model (didokumentasikan, tidak dipakai ula
 BATCH_SIZE = 128     # batch size training model (didokumentasikan, tidak dipakai ulang)
 OPTIMIZER = "adam"   # optimizer training model (tersimpan di file .keras)
 
-MODEL_PATH = os.path.join("results", "models", "model_cnn_mnist.keras")
+MODEL_PATH = os.path.join("results", "02_cnn", "models", "model_cnn_mnist.keras")
-FIGURES_DIR = os.path.join("results", "figures")
+FIGURES_DIR = os.path.join("results", "02_cnn", "figures")
-REPORTS_DIR = os.path.join("results", "reports")
+REPORTS_DIR = os.path.join("results", "02_cnn", "reports")
 
 CLASS_NAMES = [str(i) for i in range(10)]
 
@@ -88,7 +88,7 @@ def main():
     if not os.path.exists(MODEL_PATH):
         raise FileNotFoundError(
             f"Model tidak ditemukan di '{MODEL_PATH}'. "
-            f"Jalankan dahulu experiments/05_cnn_full_pipeline.py (skrip Anggota 1)."
+            f"Jalankan dahulu tasks/cnn/05_cnn_full_pipeline.py (skrip Anggota 1)."
         )
     model = keras.models.load_model(MODEL_PATH)
     print(f"  -> Model '{model.name}' berhasil dimuat ({model.count_params():,} parameter).")

tasks/mlp/01_mlp_comparison.py

@@ -1,9 +1,11 @@
 import sys
 import os
 import time
+import pandas as pd
+import matplotlib.pyplot as plt
 
 # Tambahkan root directory ke path agar dapat import dari src
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+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
@@ -93,6 +95,42 @@ def main():
     print(f"Jumlah Parameter    : {best_model['parameters']:,}")
     print(f"Waktu Training/Epoch: {best_model['time_per_epoch']:.2f} detik")
 
+    # 5. Simpan Hasil ke Folder results/01_mlp
+    REPORTS_DIR = os.path.join("results", "01_mlp", "reports")
+    FIGURES_DIR = os.path.join("results", "01_mlp", "figures")
+    os.makedirs(REPORTS_DIR, exist_ok=True)
+    os.makedirs(FIGURES_DIR, exist_ok=True)
+
+    df_results = pd.DataFrame(results)
+    csv_path = os.path.join(REPORTS_DIR, "perbandingan_mlp.csv")
+    df_results.to_csv(csv_path, index=False)
+    print(f"\n[OK] Ringkasan CSV disimpan ke : {csv_path}")
+
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
+    names = ["Model A\n(1 Layer)", "Model B\n(2 Layer)", "Model C\n(3 Layer)"]
+    accs = [r["accuracy"] for r in results]
+    bars1 = ax1.bar(names, accs, color=["#4C72B0", "#55A868", "#C44E52"])
+    ax1.set_title("Perbandingan Test Accuracy (%)", fontsize=12, fontweight="bold")
+    ax1.set_ylabel("Accuracy (%)")
+    ax1.set_ylim([max(0, min(accs) - 2), 100])
+    for bar in bars1:
+        yval = bar.get_height()
+        ax1.text(bar.get_x() + bar.get_width()/2, yval + 0.2, f"{yval:.2f}%", ha='center', va='bottom')
+
+    times = [r["time_per_epoch"] for r in results]
+    bars2 = ax2.bar(names, times, color=["#4C72B0", "#55A868", "#C44E52"])
+    ax2.set_title("Waktu Training per Epoch (detik)", fontsize=12, fontweight="bold")
+    ax2.set_ylabel("Detik / Epoch")
+    for bar in bars2:
+        yval = bar.get_height()
+        ax2.text(bar.get_x() + bar.get_width()/2, yval + 0.05, f"{yval:.2f}s", ha='center', va='bottom')
+
+    plt.tight_layout()
+    fig_path = os.path.join(FIGURES_DIR, "perbandingan_3_model_mlp.png")
+    plt.savefig(fig_path, dpi=130)
+    plt.close()
+    print(f"[OK] Grafik perbandingan disimpan ke: {fig_path}")
+
 
 if __name__ == "__main__":
     main()

tasks/mlp/02_regularization.py

@@ -5,7 +5,7 @@ matplotlib.use("Agg")
 import matplotlib.pyplot as plt
 
 # Tambahkan root directory ke path agar dapat import dari src
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+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, create_regularized_mlp_model
@@ -50,14 +50,14 @@ def main():
     )
 
     # 4. Save Model Artifact
-    results_model_dir = os.path.join("results", "models")
+    results_model_dir = os.path.join("results", "01_mlp", "models")
     ensure_dir(results_model_dir)
     model_reg_path = os.path.join(results_model_dir, "model_c_regularized.keras")
     model_reg.save(model_reg_path)
     print(f"\n[INFO] Model teregularisasi disimpan di: {model_reg_path}")
 
     # 5. Plot Comparison Curves
-    results_fig_dir = os.path.join("results", "figures")
+    results_fig_dir = os.path.join("results", "01_mlp", "figures")
     ensure_dir(results_fig_dir)
     fig_path = os.path.join(results_fig_dir, "loss_regularization.png")
 

tasks/nlp_sentiment/08_sentimen_gap.py

@@ -32,9 +32,9 @@ EPOCHS = 20
 BATCH_SIZE = 64
 RANDOM_STATE = 42
 
-FIGURES_DIR = "results/figures"
+FIGURES_DIR = os.path.join("results", "03_nlp_sentiment", "figures")
-REPORTS_DIR = "results/reports"
+REPORTS_DIR = os.path.join("results", "03_nlp_sentiment", "reports")
-MODELS_DIR = "results/models"
+MODELS_DIR = os.path.join("results", "03_nlp_sentiment", "models")
 
 os.makedirs(FIGURES_DIR, exist_ok=True)
 os.makedirs(REPORTS_DIR, exist_ok=True)