GitHub - rbwtech/lacak-nutri

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Platform Analisis Nutrisi dan Validasi BPOM Berbasis Artificial Intelligence

Transparansi Instan, Keputusan Terinformasi

NAVIGATION / TABLE OF CONTENTS

Overview • Demo • Features • Security • Tech Stack • Installation • Design System • API Docs • License • Team • Acknowledgement • Support

Overview

LacakNutri adalah aplikasi web berbasis Full Stack dan AI yang dirancang untuk memberikan analisis nutrisi instan dan validasi keamanan pangan untuk produk makanan/minuman kemasan. Aplikasi ini menggunakan Vision Language Model (VLM) dari Google Gemini untuk memproses label nutrisi dan komposisi dari gambar, serta mekanisme web scraping untuk memverifikasi status registrasi produk ke database resmi BPOM.

Problem Statement: Minimnya transparansi informasi nilai gizi dan kesulitan memverifikasi keamanan produk BPOM.

Solution: Platform end-to-end yang mengubah label fisik menjadi data terstruktur dengan insight kesehatan personal dalam hitungan detik.

Core Value

Capture Image → AI Analysis (VLM) → Personalized Health Insights
QR Code → BPOM Validation (Scraping) → BPOM Result

Target Users:

Konsumen sadar kesehatan
Keluarga dengan kondisi alergi/diet khusus
Individu dengan penyakit metabolik (diabetes, hipertensi)
Masyarakat umum yang peduli keamanan pangan

DEMO ACCESS

Live Instance

URL: https://lacaknutri.rbwtech.io

Admin Dashboard (Demo)

Email: lacaknutri@rbwtech.io
Password: UINIC7.0

Capabilities:

Full CRUD operations
Real-time analytics dashboard
User management
Content moderation (GiziPedia)
Scan history monitoring

Test Credentials (Regular User)

Email: demo@lacaknutri.com
Password: demo123

CORE FEATURES

Disclaimer:
Fungsi utama /api/scan/analyze menggunakan Gemini AI (VLM) untuk membaca dan menganalisis label dari gambar secara langsung. Ini lebih dari sekadar OCR; ini adalah analisis gambar terstruktur. Tesseract hanya digunakan untuk raw text extraction di endpoint terpisah (/api/scan/ocr-text).

1. Vision-Powered Nutrition Analysis (VLM & AI)

Engine: Google Gemini 2.5 / 2.0 Flash

AI Analysis Pipeline (/api/scan/analyze):

flowchart TD
    %% Input
    U([User Uploads Image]):::input --> FE[Frontend: Get Recaptcha v3 Token]:::frontend
    FE --> API[[API Request + Token]]:::api

    %% Security Layer
    subgraph SECURITY["Security Layer"]
      RL{Rate Limit?}:::decision
      CV{Captcha Score?}:::decision
    end
    API --> RL
    RL -- "Too Many (IP)" --> ERR429([429: Too Many Requests]):::error
    RL -- "OK" --> CV
    CV -- "Score < 0.5" --> ERR403([403: Forbidden]):::error
    CV -- "Score >= 0.5" --> GEM["analyze_nutrition_image (GeminiService)"]:::service

    %% Main Analysis
    GEM -- "Prompt + Image" --> GVLM["Gemini VLM Analysis"]:::ai
    GVLM -- "Structured JSON" --> BE[Backend Service]:::backend
    BE -- "Extract Ingredients" --> ALRGY{User Allergies Found?}:::decision

    ALRGY -- "Allergens Detected" --> HIST[Create ScanHistoryOCR]:::storage
    HIST --> SHOW([Display Scan Result]):::result

    ALRGY -- "No Allergens" --> SHOW

    %% Assignment of classes
    class U input;
    class FE frontend;
    class API api;
    class RL,CV,ALRGY decision;
    class GEM service;
    class GVLM ai;
    class BE backend;
    class SHOW result;
    class ERR429,ERR403 error;
    class HIST storage;
    class SECURITY SECURITY;

Hasil Structured JSON yang Dikelola oleh AI Service:

Field	Tipe Data	Deskripsi
`nutrition`	JSON Object	Data nutrisi terstruktur (kalori, protein, lemak, gula, sodium, dll.).
`health_score`	Integer (0-100)	Penilaian kesehatan objektif oleh AI.
`grade`	String (A-E)	Kategori nilai kesehatan berdasarkan `health_score`.
`summary`	String	Ringkasan analisis nutrisi (2-3 kalimat).
`pros`, `cons`	List of Strings	Keunggulan dan kelemahan nutrisi yang terdeteksi.
`ingredients`	String	Daftar bahan yang diekstraksi.
`warnings`	List of Strings	Daftar potensi peringatan (misalnya, Tinggi Gula, Aditif) ditambah alergen yang terdeteksi dari profil pengguna.

Rate Limiting (Keterbatasan Penggunaan)

Untuk menjaga resource AI, endpoint /api/scan/analyze memiliki batasan penggunaan harian:

Pengguna Tamu (Guest) atau Pengguna Terdaftar Biasa: Maksimal 10x Analisis AI per hari.
Administrator: Tidak memiliki batasan.

2. BPOM Validation

Fitur ini memvalidasi produk menggunakan nomor registrasi BPOM yang diinput (MD/ML/SI/DBL) atau dipindai QR-nya.

Mekanisme: Web Scraping (menggunakan httpx dan BeautifulSoup4) dari situs resmi BPOM (https://cekbpom.pom.go.id).

Alur Validasi BPOM (/api/scan/bpom):

sequenceDiagram
    participant User
    participant Frontend
    participant API as Backend API
    participant Google as Google Server
    participant CRUD as Database/Cache
    participant BPOMScraper

    User->>Frontend: Input BPOM / Click Scan
    activate Frontend
    Frontend->>Google: Request reCAPTCHA v3 Token
    Google-->>Frontend: Return Token

    Frontend->>+API: POST /scan/bpom (BPOM No + Token)

    Note over API: 1. Check Rate Limit (IP Based)<br/>2. Validate Captcha Token

    API->>Google: Verify Token (Server-to-Server)
    Google-->>API: { success: true, score: 0.9 }

    alt Score Low or Rate Limit Hit
        API-->>Frontend: 403 Forbidden / 429 Too Many Requests
        Frontend-->>User: Show Error Message
    else Security Passed
        API->>+CRUD: Check cache (ScanHistoryBPOM)

        alt Data Cached
            CRUD-->>API: Return cached data
            API->>CRUD: Create Scan History (No scraping)
        else Cache Miss
            API->>+BPOMScraper: Search BPOM (Scraping)
            BPOMScraper-->>API: Product details
            API->>CRUD: Create BPOM Cache
            API->>CRUD: Create Scan History
        end

        API-->>-Frontend: Validation Result JSON
        Frontend-->>User: Display Product Data
    end
    deactivate Frontend

Benefit Caching: Hasil scraping disimpan dalam database (bpom_cache) untuk menghemat resource dan memberikan respons cepat.

Sample Response:

{
  "success": true,
  "data": {
    "registration_number": "MD 272831023097",
    "product_name": "Indomie Goreng",
    "brand": "PT Indofood CBP Sukses Makmur Tbk",
    "status": "TERDAFTAR",
    "expiry_date": "2028-12-31",
    "cached": false
  }
}

3. Allergen Cross-Reference (Personalisasi)

Endpoint /api/scan/analyze secara otomatis memeriksa bahan-bahan yang diidentifikasi oleh AI terhadap daftar alergi yang tersimpan di profil pengguna.

Daftar alergi pengguna diambil dari database (current_user.allergies).
Daftar bahan (ingredients) dari hasil AI diubah menjadi huruf kecil dan diperiksa keberadaan alergen yang telah diatur oleh pengguna.
Alergen yang terdeteksi ditambahkan ke dalam field warnings sebelum disimpan ke ScanHistoryOCR dan ditampilkan kepada pengguna.

4. Food Catalog (Database Makanan)

Akses cepat ke database internal yang berisi 4.122 data produk makanan dan minuman yang telah terkurasi.

Fitur Utama:

Pencarian Cepat (/api/food/search): Mencari produk berdasarkan nama atau merek.
Detail Nutrisi: Menampilkan informasi nilai gizi (AKG), komposisi, dan takaran saji tanpa perlu melakukan scan ulang.
Pagination: Mendukung infinite scroll atau pagination untuk menjelajahi katalog.

Data Point:

4.122 entri produk unik.
Mencakup kategori: Makanan Ringan, Minuman, Produk Susu, Makanan Instan, dll.

5. Favorites Management

Pengguna dapat menyimpan produk hasil scan atau pencarian ke dalam daftar favorit untuk akses cepat di kemudian hari.

Endpoint: /api/favorites
Fungsi: Simpan (POST), Hapus (DELETE), dan Lihat Daftar (GET) produk favorit.
Integrasi: Terhubung langsung dengan Food Catalog dan Scan History.

6. GiziPedia (Education Hub)

Content Structure:

Categories: Zat Gizi Makro, Zat Gizi Mikro, Aditif Makanan, Label & Istilah
Articles: 100 artikel edukasi
Format: Markdown-based dengan images
Search: Full-text search dengan relevance ranking

Features:

Rich text editor (admin)
Category filtering
Bookmark system
Reading time estimate
Related articles

7. Allergen Management System

Manajemen data alergen yang dinamis.

Allergen Database: Admin dapat mengelola master data alergen (cth: Kacang, Susu, Gluten, Seafood) yang nantinya dipilih oleh user di profil mereka.

Detection Logic (Actual Implementation):

# app/routers/scan.py
user_allergies = [allergy.name.lower() for allergy in current_user.allergies]
ingredients_text = result.get('ingredients', '').lower()

detected_allergens = [
    allergy.capitalize()
    for allergy in user_allergies
    if allergy in ingredients_text
]
# Result: ["Kacang Tanah", "Gluten"]

8. Scan History & Analytics

Setiap aktivitas scan (OCR maupun BPOM) disimpan untuk referensi pengguna.

Riwayat Terpisah: Tab khusus untuk Riwayat OCR dan Riwayat BPOM.
Detail View: Pengguna dapat melihat kembali detail nutrisi dan hasil analisis AI dari produk yang pernah discan sebelumnya.
Admin Analytics: Admin dapat memantau jumlah scan harian untuk mengevaluasi penggunaan sistem.

SECURITY & PROTECTION MECHANISMS

Aplikasi ini menerapkan standar keamanan tinggi untuk mencegah abuse dan brute-force attacks.

1. Google reCAPTCHA v3 (Invisible / Smart)

Sistem membedakan antara manusia dan bot tanpa mengganggu pengalaman pengguna (tanpa puzzle "pilih lampu merah").

Frontend: Menggunakan react-google-recaptcha-v3 untuk menghasilkan token berdasarkan interaksi user.
Backend: Middleware memverifikasi token ke server Google sebelum memproses request sensitif.
Penerapan: Login, Register, Forgot Password, Scan BPOM, dan Scan AI.

2. IP-Based Rate Limiting

Menggunakan slowapi untuk membatasi jumlah request dari satu IP address dalam periode waktu tertentu.

Login/Register/Reset Password: Maksimal 5 request/menit (Mencegah Brute Force).
Forgot Password: Maksimal 3 request/jam (Mencegah Spam Email).
AI Analysis: Maksimal 10 request/hari (Guest / User) untuk menghemat kuota API Gemini.

3. Session & Quota Management

Guest Mode: User tanpa login bisa mencoba scan terbatas (dilacak via IP & Session Header).
Registered User: Riwayat tersimpan permanen.

TECHNOLOGY STACK

Frontend Architecture

React 18 (Vite 6)
├── Routing: React Router v7
├── State: TanStack Query v5 + Context API
├── UI: Tailwind CSS 3 + Framer Motion (Animation) + Lucide React (Icons)
├── Forms: React Hook Form + Zod validation
├── HTTP: Axios
├── Scanner: html5-qrcode
├── Charts: Chart.js + react-chartjs-2
└── Build: Vite 6 (ES modules)

Key Dependencies (package.json):

{
  "react": "^18.3.1",
  "vite": "^6.0.5",
  "react-router-dom": "^7.1.1",
  "@tanstack/react-query": "^5.64.1",
  "tailwindcss": "^3.4.17",
  "axios": "^1.7.9",
  "html5-qrcode": "^2.3.8",
  "chart.js": "^4.4.7",
  "react-chartjs-2": "^5.3.0",
  "react-hook-form": "^7.54.2",
  "zod": "^3.24.1",
  "framer-motion": "^11.16.0",
  "lucide-react": "^0.471.0"
}

Folder Structure:

frontend/
├── public/
└── vite.config.js

frontend/src/
├── assets/
├── components/
│   ├── layouts/        # Header, Footer, MainLayout
│   ├── scanner/        # Komponen Scanner Modular
│   │   ├── CameraView.jsx
│   │   └── ScanResult.jsx
│   └── ui/             # Reusable UI Components (Button, Card, Modal, dll)
├── config/             # Konfigurasi Axios/API
├── context/            # Global State (AuthContext)
├── hooks/              # Custom Hooks
│   ├── useScannerCamera.js
│   ├── useSmartCaptcha.js
│   └── ...
├── i18n/               # Multi-language (ID/EN)
├── pages/              # Halaman Aplikasi
│   ├── admin/          # Dashboard Admin
│   ├── Scanner.jsx     # Halaman Utama Scanner
│   ├── Login.jsx
│   └── ...
├── routes/             # Routing & Protection (ProtectedRoute)
└── utils/              # Helper functions

Backend Architecture

FastAPI + Python 3.11
├── ORM: SQLAlchemy 2.0
├── Database: PyMySQL (MariaDB connector)
├── Validation: Pydantic v2
├── Auth: JWT (python-jose) + Passlib (bcrypt)
├── AI: google-genai (Google DeepMind SDK terbaru)
├── OCR: Pytesseract (Tesseract OCR Wrapper)
├── Scraping: HTTPX + BeautifulSoup4
├── Image Processing: Pillow
└── Server: Uvicorn (ASGI)

Key Dependencies (requirements.txt):

fastapi>=0.100.0
uvicorn>=0.20.0
python-multipart>=0.0.6
sqlalchemy>=2.0.0
pymysql>=1.0.0
pydantic>=2.0.0
pydantic-settings>=2.0.0
pytesseract
pytz
slowapi
email-validator>=2.0.0
python-jose[cryptography]>=3.3.0
passlib>=1.7.4
bcrypt==4.0.1
python-dotenv>=1.0.0
google-genai>=0.1.0
beautifulsoup4>=4.12.0
httpx>=0.24.0
pillow>=10.0.0
requests>=2.31.0

Project Structure:

Backend (FastAPI)
backend/
├── app/
│   ├── core/           # Konfigurasi inti (DB, Security, Limiter)
│   │   ├── config.py
│   │   ├── database.py
│   │   ├── limiter.py  <-- Rate Limiter Instance
│   │   └── security.py
│   ├── crud/           # Database Operations (Create, Read, Update, Delete)
│   ├── models/         # SQLAlchemy ORM Models (Tabel Database)
│   ├── routers/        # API Endpoints (Controller)
│   │   ├── auth.py     <-- Login/Register logic
│   │   ├── scan.py     <-- Logic Scanner Utama
│   │   └── ...
│   ├── schemas/        # Pydantic Models (Data Validation)
│   ├── services/       # External Services Logic
│   │   ├── ai_service.py      <-- Integrasi Google Gemini
│   │   └── bpom_endpoint.py   <-- Scraper BPOM
│   └── dependencies.py # Dependency Injection (Auth, Captcha)
├── main.py             # Entry Point Aplikasi
└── requirements.txt

Database Schema

Technology: MariaDB 11.4.4 (via PyMySQL)

Schema Design:

-- Users & Authentication
users (14 columns)
├── id (PK)
├── email (UNIQUE), password_hash, full_name
├── role (enum: user, admin, owner)
├── gender, age, height, weight
├── activity_level (enum: sedentary, light, moderate, active, very_active)
├── reset_token, reset_token_expires
└── created_at, updated_at

-- Reference Data
allergens (
  id, name, code, category, description,
  severity_level, created_by, timestamps
)
additives (
  id, name, code, category, safety_level,
  description, health_risks, timestamps
)

-- User Relations
user_allergies (id, user_id, allergen_id, notes)
favorites (
  id, user_id,
  food_id (FK -> foods),
  scan_ocr_id (FK -> scan_history_ocr),
  scan_bpom_id (FK -> scan_history_bpom),
  created_at
)

-- Core Data: Scan History
scan_history_ocr (14 columns)
├── id (PK), user_id (FK)
├── image_data (LONGTEXT/Base64)
├── product_name
├── ocr_raw_data (JSON)      -- Stores calories, protein, etc.
├── ai_analysis (TEXT)       -- AI Summary
├── pros (JSON), cons (JSON)
├── ingredients (TEXT)
├── warnings (JSON)          -- Includes allergen warnings
├── health_score (INT), grade (VARCHAR)
└── is_favorited (BOOL), created_at

scan_history_bpom (10 columns)
├── id (PK), user_id (FK)
├── bpom_number, product_name, brand, manufacturer
├── status (TERDAFTAR, dll)
├── raw_response (JSON)      -- Full scraped data
└── is_favorited (BOOL), created_at

-- Core Data: Food Catalog
foods (18 columns)
├── id (PK)
├── name, brand, barcode
├── serving_size, calories, proteins, fats, carbs
├── fiber, sugar, sodium
├── ingredients, image_url
├── external_id, data_source
└── timestamps

-- Caching & Content
bpom_cache (id, bpom_number, data (JSON), timestamps)
articles (id, title, slug, content, category_id, status, views, timestamps)
article_categories (id, name, slug, description)

ERD Logic (Simplified):

      [foods] <───────┐
                      │
  [scan_history_ocr] ─┼──► [favorites] ◄── [users] ──► [user_allergies] ──► [allergens]
                      │
 [scan_history_bpom] ─┘

Infrastructure & Deployment

Frontend Hosting:

Platform: VPS (Dedicated Server)
Web Server: Nginx 1.24
SSL: Let's Encrypt
Deployment: Manual Build (dist/) -> Nginx Serve

Backend Hosting:

Setup: Systemctl
Runtime: Python 3.11
Server: Uvicorn
Environment: Production

Database:

Server: VPS (aaPanel Managed)
DBMS: MariaDB 11.4.4
Connection: Remote (PyMySQL) via Host IP

QUICK START (INSTALLATION)

Prerequisites

Node.js 18+
Python 3.11+
MariaDB 11.4.4
Git

1. Clone Repository

git clone https://github.com/rbwtech/lacak-nutri.git
cd lacak-nutri

2. Database Setup

Create Database:

CREATE DATABASE lacak_nutri CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;

Import Schema:

Pastikan Anda berada di root directory project (lacak-nutri/).

mysql -u root -p lacak_nutri < database/schema.sql
mysql -u root -p lacak_nutri < database/seed.sql

3. Backend Configuration

Install Dependencies:

cd backend
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

Environment Variables (.env):

Buat file .env di dalam folder backend/ dan isi sesuai konfigurasi berikut:

DB_HOST=localhost
DB_PORT=3306
DB_NAME=lacak_nutri
DB_USER=root
DB_PASSWORD=your_password

SECRET_KEY=generate_your_secure_secret_key_here
ALGORITHM=HS256
ACCESS_TOKEN_EXPIRE_MINUTES=43200

GEMINI_API_KEY=your_google_gemini_api_key

UPLOAD_DIR=./uploads
MAX_UPLOAD_SIZE=10485760

CORS_ORIGINS=http://localhost:5173

DEBUG=True
HOST=0.0.0.0
PORT=8000

RECAPTCHA_SECRET_KEY=your_recaptcha_v3_server_secret
EMAIL_SENDER_NAME="Lacak Nutri Admin"
EMAIL_SENDER_ADDRESS=admin@lacaknutri.com

SMTP_SERVER=smtp.gmail.com
SMTP_PORT=587
SMTP_USERNAME=your_email@gmail.com
SMTP_PASSWORD=your_app_password

Run Server:

Pastikan virtual environment aktif dan Anda berada di folder backend/.

uvicorn main:app --reload --host 0.0.0.0 --port 8000

Server: http://localhost:8000 API Docs: http://localhost:8000/docs

4. Frontend Configuration

Install Dependencies:

Buka terminal baru dan masuk ke folder frontend.

cd frontend
npm install

Environment Variables (.env):

Buat file .env di dalam folder frontend/.

VITE_API_URL=http://localhost:8000/api
VITE_RECAPTCHA_SITE_KEY=your_recaptcha_v3_client_site_key

Run Development Server:

npm run dev

Frontend: http://localhost:5173

5. Production Build

Frontend:

npm run build
# Output akan berada di folder: dist/

Backend:

# Menggunakan Gunicorn (Linux/Mac)
gunicorn main:app -w 4 -k uvicorn.workers.UvicornWorker --bind 0.0.0.0:8000

# Atau Uvicorn langsung (Windows/Simple Deploy)
uvicorn main:app --host 0.0.0.0 --port 8000 --workers 4

DESIGN SYSTEM

Sistem desain LacakNutri menggunakan tema kustom Tailwind CSS dengan dukungan penuh untuk Light Mode dan Dark Mode.

Color Palette

Warna dikelola menggunakan CSS Variables untuk mendukung pergantian tema yang mulus.

Color Token	Light Mode (Hex)	Dark Mode (Hex)	Penggunaan Utama
Primary	`#FF9966`	`#FF9966`	Brand identity, CTA buttons, links
Primary Hover	`#FF7A4D`	`#FF7A4D`	Hover states
Secondary	`#6B8E23`	`#8ABE53`	Status sehat, verifikasi (adjusted for contrast)
Accent	`#A1D2D5`	`#5D8A8D`	Dekorasi, info cards, highlights
Background Base	`#FDFDF5`	`#121212`	Latar belakang utama aplikasi
Background Surface	`#FFFFF7`	`#1E1E1E`	Kartu (Cards), Modal, Sidebar
Text Primary	`#333333`	`#E0E0E0`	Judul, konten utama
Text Secondary	`#8C8C8C`	`#A0A0A0`	Label, metadata, deskripsi singkat
Border	`#EBE3D5`	`#333333`	Garis pemisah, border input

Status Colors (Hardcoded):

Success: #4CAF50
Warning: #FFC107
Error: #EF5350

Typography

Font Family: Menggunakan Manrope dari Google Fonts sebagai font utama, dengan fallback ke Inter dan sans-serif.

font-family: "Manrope", "Inter", "sans-serif";

Weights: 400 (Regular), 500 (Medium), 600 (SemiBold), 700 (Bold), 800 (ExtraBold).

UI Components Styling

Konfigurasi Tailwind (tailwind.config.js) mencakup kustomisasi berikut:

Border Radius:

rounded-xl: 12px
rounded-2xl: 16px
rounded-3xl: 24px

Box Shadow:

shadow-soft: 0 8px 24px rgba(0, 0, 0, 0.05)

Animations:

Blob: Animasi background fluid (blob 7s infinite).
Fade In: Transisi masuk elemen (fadeIn 0.5s ease-out).

LICENSE

This project is licensed under the MIT License.

MIT License

Copyright (c) 2025 Trio WakwaW Team

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.

TEAM

Trio WakwaW Team - UINIC 7.0 Web Development Competition

┌─────────────────────────────────────────────────────┐
│  Made with ❤️ by Trio WakwaW Team
│
│  • Radipta Basri Wijaya
│  • Agung Nugraha
│  • Bayu Wicaksono
│
└─────────────────────────────────────────────────────┘

ACKNOWLEDGEMENT

Technologies:

FastAPI - Modern Python web framework
React - UI library
Gemini AI - Generative AI analysis
BPOM - Public product registration database

Inspiration:

MyFitnessPal - Nutrition tracking
Open Food Facts - Open food database
BPOM Mobile - Product verification

Competition:

UINIC 7.0 Web Development - Universitas Islam Negeri Sunan Kalijaga Yogyakarta

SUPPORT

Issues & Bugs:
Report via GitHub Issues

Feature Requests:
Submit via GitHub Discussions

General Inquiries:
WhatsApp: wa.me/6285182381003

⭐ Star us on GitHub if you find this project useful!

Name		Name	Last commit message	Last commit date
Latest commit History 149 Commits
backend		backend
database		database
frontend		frontend
API_DOCUMENTATION.MD		API_DOCUMENTATION.MD
README.md		README.md

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Platform Analisis Nutrisi dan Validasi BPOM Berbasis Artificial Intelligence

NAVIGATION / TABLE OF CONTENTS

Overview

Core Value

DEMO ACCESS

Live Instance

Admin Dashboard (Demo)

Test Credentials (Regular User)

CORE FEATURES

1. Vision-Powered Nutrition Analysis (VLM & AI)

Rate Limiting (Keterbatasan Penggunaan)

2. BPOM Validation

3. Allergen Cross-Reference (Personalisasi)

4. Food Catalog (Database Makanan)

5. Favorites Management

6. GiziPedia (Education Hub)

7. Allergen Management System

8. Scan History & Analytics

SECURITY & PROTECTION MECHANISMS

1. Google reCAPTCHA v3 (Invisible / Smart)

2. IP-Based Rate Limiting

3. Session & Quota Management

TECHNOLOGY STACK

Frontend Architecture

Backend Architecture

Database Schema

Infrastructure & Deployment

QUICK START (INSTALLATION)

Prerequisites

1. Clone Repository

2. Database Setup

3. Backend Configuration

4. Frontend Configuration

5. Production Build

DESIGN SYSTEM

Color Palette

Typography

UI Components Styling

LICENSE

TEAM

ACKNOWLEDGEMENT

SUPPORT

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