System Architecture
- Next.js App Router → modern full-stack architecture dengan Server Components, API Routes, dan Server Actions.
- TypeScript → type-safe development untuk meningkatkan maintainability dan reliability.
- PostgreSQL → relational database berperforma tinggi untuk pengelolaan data atlet, nutrisi, dan rekomendasi.
- Prisma ORM → type-safe database access dengan relational query yang efisien.
- React Query (TanStack Query) → server state management, intelligent caching, optimistic updates, dan background synchronization.
- Zod Validation → end-to-end schema validation untuk API, form, dan business logic.
- JWT Authentication → secure token-based authentication.
- Role Based Access Control (RBAC) → manajemen hak akses berdasarkan role pengguna.
- REST API → komunikasi antara frontend dan backend.
- Collaborative Filtering Engine → personalized nutrition recommendation menggunakan cosine similarity.
- Responsive Dashboard → optimal untuk desktop maupun tablet.
AI Recommendation Engine
Platform ini mengimplementasikan Collaborative Filtering Recommendation System untuk menghasilkan rekomendasi nutrisi yang dipersonalisasi berdasarkan histori penilaian makanan dan kemiripan preferensi antar atlet.
Engine melakukan proses:
- Athlete Profiling
- Food Rating Analysis
- Cosine Similarity Calculation
- Similar Athlete Matching
- Recommendation Scoring
- Personalized Food Ranking
- Nutrition Filtering
- Daily Nutrition Recommendation
- Recommendation Analytics
Core Modules
Authentication & Authorization
- Login
- JWT Authentication
- Secure Session
- Password Encryption
- Role Based Access Control (RBAC)
Dashboard
- Dashboard Overview
- Athlete Summary
- AI Recommendation Status
- Recommendation Statistics
- Recommendation Analytics
- Model Performance
- Recommendation Accuracy
Athlete Management
- Athlete Management
- Athlete Profile
- Body Weight
- Sport Category
- Training Phase
- Daily Calories
- Nutrition Profile
Food Database
- Food Management
- Nutrition Information
- Calories
- Protein
- Carbohydrates
- Sugar
- Food Categories
- Search & Filtering
AI Recommendation
- Collaborative Filtering
- Cosine Similarity
- Personalized Recommendation
- Food Ranking
- Recommendation History
- Recommendation Score
- Nutrition Filtering
Nutrition Planning
- Personalized Nutrition Plan
- Daily Meal Planning
- Breakfast
- Lunch
- Dinner
- Snack Planning
- Calorie Distribution
- Protein Prioritization
Favorite Foods
- Favorite Collection
- Saved Recommendation
- Personalized Nutrition Collection
- Favorite Tracking
Recommendation Analytics
- Recommendation Accuracy
- Recommendation Acceptance
- Athlete Engagement
- Prediction Score
- Recommendation Trends
- Model Performance Dashboard
Technology Stack
Frontend
- Next.js (App Router)
- React
- TypeScript
- Tailwind CSS
- Shadcn/UI
- Radix UI
- React Query (TanStack Query)
- React Hook Form
- Zod
- Axios
- Recharts
- Lucide React
Backend
- Python
- REST API
- JWT Authentication
- Role Based Access Control (RBAC)
- Recommendation Engine
- Authentication Middleware
Database
- PostgreSQL
- Prisma ORM
AI Engine
- Collaborative Filtering
- Cosine Similarity Algorithm
- Recommendation Scoring
- Personalized Recommendation
- Nutrition Profiling
- Similarity Analysis
Database Layer
Menggunakan PostgreSQL dengan struktur database relasional yang dirancang scalable, normalized, dan mudah dikembangkan.
Mencakup entitas utama seperti:
- users
- roles
- athlete_profiles
- foods
- food_categories
- food_ratings
- nutrition_profiles
- recommendation_results
- recommendation_histories
- nutrition_plans
- favorite_foods
- analytics_logs
- audit_logs
Seluruh relasi database dibangun menggunakan Prisma ORM sehingga menghasilkan query yang type-safe, efisien, dan mudah dipelihara.
Performance & Security
- Next.js Server Components
- API Route Optimization
- PostgreSQL Query Optimization
- Prisma Query Optimization
- React Query Smart Caching
- JWT Authentication
- Role Based Access Control (RBAC)
- Middleware Security
- Zod Validation
- Secure REST API
- Type-safe Development
- Responsive Dashboard
- Optimized Recommendation Engine
Value
- AI-Powered Nutrition Recommendation
- Collaborative Filtering Engine
- Personalized Athlete Nutrition
- Nutrition Planning Dashboard
- Athlete Performance Monitoring
- Food Recommendation Analytics
- Intelligent Food Ranking
- Centralized Nutrition Management
- Secure Web-Based Platform
- Enterprise Scalable Architecture
Client & Implementation
Project ini dikembangkan untuk Bangrajan Muay Thai sebagai web-based AI nutrition recommendation platform yang membantu atlet memperoleh rekomendasi nutrisi secara personal berdasarkan profil pengguna, jenis olahraga, fase latihan, kebutuhan kalori, serta histori penilaian makanan menggunakan algoritma Collaborative Filtering dan Cosine Similarity.
Platform menyediakan dashboard terintegrasi untuk mengelola data atlet, database makanan, profil nutrisi, rekomendasi berbasis AI, perencanaan nutrisi harian, analitik performa model, serta monitoring hasil rekomendasi dalam satu sistem berbasis web.
Aplikasi dikembangkan menggunakan Next.js App Router, TypeScript, PostgreSQL, Prisma ORM, React Query, Zod, Tailwind CSS, Shadcn/UI, Radix UI, serta REST API dengan JWT Authentication dan Role Based Access Control (RBAC) sehingga menghasilkan aplikasi yang aman, responsif, dan mudah dikembangkan.
Saya bertanggung jawab secara end-to-end mulai dari UI/UX Design, System Architecture, Database Design, AI Recommendation Engine (Collaborative Filtering & Cosine Similarity), Frontend Development, Backend Development, REST API Development, Authentication & Authorization, Database Optimization, hingga implementasi seluruh fitur utama sehingga sistem siap digunakan sebagai platform rekomendasi nutrisi berbasis Artificial Intelligence.