🎵 Noise Pollution Detection System

Sistem deteksi dan analisis polusi suara menggunakan Machine Learning dengan arsitektur fullstack modern.

🏗️ Arsitektur Sistem

Backend: Django REST API dengan MySQL database
Frontend: React TypeScript dengan Material-UI components
ML Models: CatBoost dengan optimasi algoritma bio-inspired
Audio Processing: Librosa untuk ekstraksi fitur audio

✨ Fitur Utama

🔊 Upload & Analisis Audio: Drag & drop file audio untuk analisis real-time
📊 Prediksi Noise Level: Prediksi tingkat kebisingan dalam decibel (dB)
🏭 Klasifikasi Sumber: Identifikasi sumber kebisingan (traffic, construction, industrial, dll)
🏥 Analisis Dampak Kesehatan: Evaluasi potensi dampak kesehatan dari tingkat kebisingan
📈 Dashboard Real-time: Visualisasi data dan analytics dengan charts interaktif
📝 History & Tracking: Riwayat analisis dan tracking perubahan dari waktu ke waktu
🎯 Model Optimization: Model ML yang dioptimasi menggunakan algoritma Firefly & Fruit Fly
🔄 REST API: API endpoints lengkap untuk integrasi dengan sistem lain

🚀 Quick Start

📋 Prerequisites

Pastikan Anda sudah menginstall:

Python 3.11+ (Download)
Node.js 18+ (Download)
MySQL 8.0+ (Download)
Git (Download)

🔧 Installation

1. Clone Repository

git clone https://github.com/yourusername/noise-pollution-detection.git
cd noise-pollution-detection

2. Setup Database (MySQL)

-- Buat database MySQL
CREATE DATABASE econoise_db CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;

-- Buat user (opsional)
CREATE USER 'econoise_user'@'localhost' IDENTIFIED BY 'your_password';
GRANT ALL PRIVILEGES ON econoise_db.* TO 'econoise_user'@'localhost';
FLUSH PRIVILEGES;

3. Setup Backend (Django)

# Masuk ke folder backend
cd backend

# Buat virtual environment
python -m venv venv

# Aktifkan virtual environment
# Windows:
venv\Scripts\activate
# Linux/Mac:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Setup environment variables
cp .env.example .env
# Edit .env file dengan konfigurasi database Anda

# Jalankan migrasi database
python manage.py migrate

# Buat superuser (opsional)
python manage.py createsuperuser

# Test server
python manage.py runserver

Backend akan berjalan di: http://localhost:8000

4. Setup Frontend (React)

# Buka terminal baru, masuk ke folder frontend
cd frontend

# Install dependencies
npm install

# Setup environment variables
cp .env.example .env
# Edit .env file jika diperlukan

# Start development server
npm start

Frontend akan berjalan di: http://localhost:3000

🎯 Testing Installation

Backend API Test:

curl http://localhost:8000/api/health/
# Should return: {"status":"healthy","timestamp":"...","service":"Noise Detection API"}

Frontend Test:
- Buka browser ke http://localhost:3000
- Anda akan melihat halaman upload dengan interface Material-UI

ML Models Test:

curl http://localhost:8000/api/models/status/
# Should return model loading status

🔧 Configuration

Backend Environment (.env)

# Django Settings
SECRET_KEY=your-secret-key-here
DEBUG=True

# Database (MySQL)
DB_NAME=econoise_db
DB_USER=root
DB_PASSWORD=your_mysql_password
DB_HOST=localhost
DB_PORT=3306

# CORS (untuk frontend)
CORS_ALLOWED_ORIGINS=http://localhost:3000,http://127.0.0.1:3000

Frontend Environment (.env)

# API Configuration
REACT_APP_API_URL=http://localhost:8000/api

# App Info
REACT_APP_APP_NAME=Noise Pollution Detection System
REACT_APP_VERSION=1.0.0

📊 API Documentation

Core Endpoints

Health Check

GET /api/health/

Response: Service health status

Model Status

GET /api/models/status/

Response: ML models loading status

Audio Analysis

POST /api/noise-detection/upload/
Content-Type: multipart/form-data

Body: audio file (WAV, MP3, FLAC, M4A)

Response:

{
  "noise_level": 65.5,
  "noise_source": "traffic",
  "health_impact": "moderate",
  "confidence": 0.85,
  "timestamp": "2025-07-07T20:30:00Z"
}

Analysis History

GET /api/noise-detection/history/

Response: List of previous analyses

Supported Audio Formats

WAV (recommended)
MP3
FLAC
M4A

Max file size: 50MB

🧠 Machine Learning Models

Model Architecture

Noise Level Prediction: Prediksi tingkat dB menggunakan CatBoost Regressor
Noise Source Classification: Klasifikasi 10 kategori sumber kebisingan
Health Impact Assessment: Evaluasi dampak kesehatan berdasarkan WHO guidelines
Optimized Models: Model yang dioptimasi menggunakan Firefly & Fruit Fly algorithms

Feature Extraction

MFCC (Mel-frequency cepstral coefficients)
Spectral features (centroid, bandwidth, rolloff)
Zero Crossing Rate
RMS Energy
Chroma features

Model Performance

Noise Level: MAE < 3 dB, R² > 0.90
Source Classification: Accuracy > 88%
Health Impact: Precision > 85%

📁 Project Structure

noise-pollution-detection/
├── backend/                    # Django REST API
│   ├── noise_detection/       # Main Django app
│   │   ├── models.py          # Database models
│   │   ├── views.py           # API views
│   │   ├── urls.py            # URL routing
│   │   └── ml_models.py       # ML model loading
│   ├── ml_models/             # Trained ML models (.pkl files)
│   ├── requirements.txt       # Python dependencies
│   ├── manage.py              # Django management
│   └── .env                   # Environment variables
├── frontend/                   # React TypeScript app
│   ├── src/
│   │   ├── components/        # React components
│   │   │   ├── HomePage.tsx   # Main upload interface
│   │   │   ├── HistoryPage.tsx # Analysis history
│   │   │   └── StatusPage.tsx # System status
│   │   ├── services/          # API service layer
│   │   │   └── api.ts         # API client
│   │   └── utils/             # Utility functions
│   ├── package.json           # Node.js dependencies
│   └── .env                   # Environment variables
├── .github/                    # GitHub templates & workflows
│   ├── workflows/             # CI/CD pipeline
│   └── ISSUE_TEMPLATE/        # Issue templates
├── .gitignore                 # Git ignore rules
├── .gitattributes             # Git attributes
├── README.md                  # This file
└── CONTRIBUTORS.md            # Contributors info

🛠️ Development

Available Scripts

Backend (Django)

cd backend

# Run development server
python manage.py runserver

# Run database migrations
python manage.py migrate

# Create new migrations
python manage.py makemigrations

# Run tests
python manage.py test

# Django shell
python manage.py shell

# Collect static files (production)
python manage.py collectstatic

Frontend (React)

cd frontend

# Start development server
npm start

# Build for production
npm run build

# Run tests
npm test

# Type checking
npx tsc --noEmit

# Linting
npm run lint --if-present

Code Quality Tools

Python (Backend)

# Code formatting
black backend/

# Import sorting
isort backend/

# Linting
flake8 backend/

# Security check
bandit -r backend/

TypeScript (Frontend)

# Type checking
npx tsc --noEmit

# ESLint
npx eslint src/

# Prettier
npx prettier --write src/

🚀 Production Deployment

Backend (Django)

Set DEBUG=False in .env
Configure proper SECRET_KEY
Setup MySQL database with proper credentials
Run python manage.py collectstatic
Use production WSGI server (gunicorn, uWSGI)
Configure reverse proxy (nginx, Apache)

Frontend (React)

Run npm run build
Serve static files from build/ directory
Configure proper API URL in production .env
Setup CDN for static assets (optional)

Environment Variables (Production)

# Backend
DEBUG=False
SECRET_KEY=your-secure-production-key
DB_HOST=your-production-db-host
DB_PASSWORD=your-secure-db-password
ALLOWED_HOSTS=yourdomain.com,www.yourdomain.com

# Frontend
REACT_APP_API_URL=https://api.yourdomain.com

📊 Monitoring & Analytics

Available Metrics

Upload Volume: Total files processed
Processing Time: Average analysis duration
Model Performance: Prediction accuracy metrics
Error Rates: API error tracking
User Activity: Usage patterns and trends

System Status Page

Visit /status untuk melihat:

✅ ML model loading status
📊 System performance metrics
🔧 Health check results
📈 Real-time statistics

🤝 Contributing

Kami menyambut kontribusi dari siapa pun! Lihat CONTRIBUTORS.md untuk panduan lengkap.

Quick Contribution Steps

Fork repository ini
Buat feature branch (git checkout -b feature/amazing-feature)
Commit changes (git commit -m 'Add amazing feature')
Push ke branch (git push origin feature/amazing-feature)
Buat Pull Request

Development Setup

Follow installation steps di atas
Install pre-commit hooks: pre-commit install
Run tests sebelum commit
Follow coding standards (Black, ESLint)

🆘 Support & Help

Common Issues

Backend Issues

Port already in use: Ganti port dengan python manage.py runserver 8001
Database connection error: Periksa MySQL service dan kredensial di .env
Missing dependencies: Run pip install -r requirements.txt

Frontend Issues

Node modules error: Hapus node_modules/ dan run npm install
Port 3000 in use: React akan otomatis suggest port lain
API connection error: Pastikan backend running di port 8000

ML Model Issues

Model loading error: Periksa file .pkl di folder ml_models/
Prediction error: Pastikan format audio file supported
Memory error: Model membutuhkan minimal 4GB RAM

Getting Help

📝 Documentation: Baca README ini secara lengkap
🐛 Bug Reports: Buat issue di GitHub dengan template yang tersedia
💡 Feature Requests: Gunakan feature request template
💬 Discussions: Join GitHub Discussions untuk tanya jawab
📧 Contact: Reach out melalui email atau social media

Useful Links

Demo: Live Demo (if available)
API Docs: API Documentation (if available)
Video Tutorial: YouTube Tutorial (if available)

🎉 Acknowledgments

CatBoost Team untuk machine learning framework
Django & React Communities untuk framework yang luar biasa
Material-UI Team untuk komponen UI yang indah
Librosa Contributors untuk audio processing library
Open Source Community untuk tools dan libraries yang digunakan

Made with ❤️ for better environmental monitoring

Sistem ini dikembangkan untuk membantu monitoring dan analisis polusi suara demi lingkungan yang lebih baik.

Name		Name	Last commit message	Last commit date
Latest commit History 112 Commits
.github		.github
ecoNoise		ecoNoise
frontend		frontend
model		model
supabase		supabase
.gitattributes		.gitattributes
.gitignore		.gitignore
README.md		README.md
dokumentasiPerbikan.md		dokumentasiPerbikan.md
fix_auth_trigger_v2.sql		fix_auth_trigger_v2.sql
fix_database_function.sql		fix_database_function.sql
map.md		map.md
package-lock.json		package-lock.json
package.json		package.json
perbaikan.md		perbaikan.md
perkembangan.md		perkembangan.md

EcoNoise/Noise-Pollution-Detection-Using-ML

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🎵 Noise Pollution Detection System

🏗️ Arsitektur Sistem

✨ Fitur Utama

🚀 Quick Start

📋 Prerequisites

🔧 Installation

1. Clone Repository

2. Setup Database (MySQL)

3. Setup Backend (Django)

4. Setup Frontend (React)

🎯 Testing Installation

🔧 Configuration

Backend Environment (.env)

Frontend Environment (.env)

📊 API Documentation

Core Endpoints

Health Check

Model Status

Audio Analysis

Analysis History

Supported Audio Formats

🧠 Machine Learning Models

Model Architecture

Feature Extraction

Model Performance

📁 Project Structure

🛠️ Development

Available Scripts

Backend (Django)

Frontend (React)

Code Quality Tools

Python (Backend)

TypeScript (Frontend)

🚀 Production Deployment

Backend (Django)

Frontend (React)

Environment Variables (Production)

📊 Monitoring & Analytics

Available Metrics

System Status Page

🤝 Contributing

Quick Contribution Steps

Development Setup

🆘 Support & Help

Common Issues

Backend Issues

Frontend Issues

ML Model Issues

Getting Help

Useful Links

🎉 Acknowledgments

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