5803025047/kelompok3-deeplearning
2
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

refactor: restrukturisasi folder eksperimen ke tasks modular

Browse Source
This commit is contained in:
matthewflrnt
2026-09-27 18:50:08 +07:00
parent e827aa0e96
commit fe63a50997
9 changed files with 112 additions and 60 deletions
Download Patch File Download Diff File Expand all files Collapse all files
README.md
+50 -36
View File
@@ -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
│ └── model_selection.md # Analisis justifikasi pemilihan model (Tugas 3)
├── src/ # Modul reusable / fungsi pembantu
├── docs/ # Dokumentasi dan analisis laporan
│ ├── laporan_akhir.md
│ ├── 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
│ ├── 01_mlp_comparison.py # Tugas 1: Perbandingan 3 arsitektur MLP
│ ├── 02_regularization.py # Tugas 2: Eksperimen Regularisasi BatchNorm + Dropout
│ ├── 03_model_selection.py # Tugas 3: Implementasi contoh arsitektur model
│ └── 04_cnn_mnist.py # Tugas 4: Implementasi CNN MNIST & perbandingan vs MLP
└── results/ # Output dari eksperimen
├── figures/ # Plot visualisasi (akurasi, loss curve, bar chart)
│ ├── hasil_cnn.png
│ └── loss_regularization.png
└── models/ # Bobot model terlatih (.keras)
├── model_cnn_mnist.keras
└── model_c_regularized.keras
├── tasks/ # Skrip tugas yang dikelompokkan berdasarkan topik
│ ├── mlp/ # Topik 1: Multi-Layer Perceptron (MNIST)
│ │ ├── 01_mlp_comparison.py # Eksperimen perbandingan 3 arsitektur MLP
│ │ ├── 02_regularization.py # Eksperimen regularisasi (BatchNorm + Dropout)
│ │ └── 03_model_selection.py # Studi kasus pemilihan arsitektur model
│ ├── cnn/ # Topik 2: Convolutional Neural Network (MNIST)
│ │ ├── 04_cnn_mnist.py # Baseline CNN vs MLP
│ │ ├── 05_cnn_full_pipeline.py # Pipeline CNN lengkap (Loss/Acc curve, Confusion Matrix)
│ │ ├── 06_cnn_deeper.py # Eksplorasi kedalaman CNN (2 blok vs 3 blok)
│ │ └── 07_cnn_evaluation.py # Evaluasi mendalam (per-class metrics, confidence, misclassified)
│ └── nlp_sentiment/ # Topik 3: NLP Analisis Sentimen (Tokopedia)
│ └── 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**
```bash
python experiments/01_mlp_comparison.py
```
#### 🔹 Topik 2: Convolutional Neural Network (CNN)
```bash
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)**
```bash
python experiments/02_regularization.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
```
#### 🔹 Topik 3: NLP Analisis Sentimen
```bash
python tasks/nlp_sentiment/08_sentimen_gap.py
```
---
experiments/04_cnn_mnist.py → tasks/cnn/04_cnn_mnist.py
+5 -5
View File
@@ -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
results_fig_dir = os.path.join("results", "figures")
# 6. Simpan grafik ke folder results/02_cnn/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
results_model_dir = os.path.join("results", "models")
# 7. Simpan model terlatih ke folder results/02_cnn/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)
experiments/05_cnn_full_pipeline.py → tasks/cnn/05_cnn_full_pipeline.py
+4 -4
View File
@@ -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__":
experiments/06_cnn_deeper.py → tasks/cnn/06_cnn_deeper.py
+3 -3
View File
@@ -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")
experiments/07_cnn_evaluation.py → tasks/cnn/07_cnn_evaluation.py
+5 -5
View File
@@ -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")
FIGURES_DIR = os.path.join("results", "figures")
REPORTS_DIR = os.path.join("results", "reports")
MODEL_PATH = os.path.join("results", "02_cnn", "models", "model_cnn_mnist.keras")
FIGURES_DIR = os.path.join("results", "02_cnn", "figures")
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).")
experiments/01_mlp_comparison.py → tasks/mlp/01_mlp_comparison.py
+39 -1
View File
@@ -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()
experiments/02_regularization.py → tasks/mlp/02_regularization.py
+3 -3
View File
@@ -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")
experiments/03_model_selection.py → tasks/mlp/03_model_selection.py
View File
experiments/08_sentimen_gap.py → tasks/nlp_sentiment/08_sentimen_gap.py
+3 -3
View File
@@ -32,9 +32,9 @@ EPOCHS = 20
BATCH_SIZE = 64
RANDOM_STATE = 42
FIGURES_DIR = "results/figures"
REPORTS_DIR = "results/reports"
MODELS_DIR = "results/models"
FIGURES_DIR = os.path.join("results", "03_nlp_sentiment", "figures")
REPORTS_DIR = os.path.join("results", "03_nlp_sentiment", "reports")
MODELS_DIR = os.path.join("results", "03_nlp_sentiment", "models")
os.makedirs(FIGURES_DIR, exist_ok=True)
os.makedirs(REPORTS_DIR, exist_ok=True)