MythEclipseandClaude
e1732f5111
- Add project plan context: team members, executive summary, schedule, risk management - Add project scope, deliverables, and classification table with symptoms - Add bibliography/references section from project plan - Centered header with logo, emoji-section navigation - Cleaner structure: Tentang → Tim → Ringkasan → Cakupan → Jadwal → Risiko → Arsitektur → Tech Stack → Memulai → Dokumentasi → Pustaka Co-Authored-By: Claude <noreply@anthropic.com>
278 lines
12 KiB
Markdown
278 lines
12 KiB
Markdown
<p align="center">
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<br>
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<img src=".github/assets/zeavis-logo.svg" alt="ZeaVis Edu" width="128"><br>
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<h1 align="center">ZeaVis Edu</h1>
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<p align="center">
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<strong>Asisten Edukasi Interaktif untuk Deteksi Penyakit Daun Jagung</strong><br>
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<em>Computer Vision • EfficientNetV2B0 • Transfer Learning</em>
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</p>
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</p>
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<p align="center">
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<a href="#-tentang"><b>Tentang</b></a> •
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<a href="#-tim"><b>Tim</b></a> •
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<a href="#-ringkasan-eksekutif"><b>Ringkasan</b></a> •
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<a href="#-cakupan--deliverables"><b>Cakupan</b></a> •
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<a href="#-jadwal"><b>Jadwal</b></a> •
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<a href="#-tech-stack"><b>Tech Stack</b></a> •
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<a href="#-memulai"><b>Memulai</b></a> •
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<a href="#-dokumentasi"><b>Dokumentasi</b></a>
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</p>
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<br>
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---
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## 🌽 Tentang
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**ZeaVis Edu** adalah aplikasi edukasi berbasis **Computer Vision** yang membantu petani, mahasiswa pertanian, dan penyuluh lapangan mengidentifikasi penyakit daun jagung secara mandiri — cukup dengan mengunggah foto daun jagung.
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Proyek ini merupakan **Capstone Project** dalam program **Pijak × IBM SkillsBuild** dengan tema **"AI for Smart Education"**, dirancang untuk menjembatani kesenjangan antara pengetahuan teori pertanian dan kebutuhan praktis di lapangan.
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---
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## 👥 Tim
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| NPM | Nama | Learning Path | Peran |
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|---|---|---|---|
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| APC246D6Y0028 | **Asep Haryana Saputra** | Back-End | Arsitektur sistem, RESTful API, deployment Docker/Cloud, keamanan upload stream |
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| APC013D6X0081 | **Selly Supriyatin** | Front-End | UI/UX responsif, mekanisme unggah gambar, modul edukasi (rekomendasi obat & penanganan) |
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| APC013D6Y0091 | **Taufik Pathurrohman** | Machine Learning | Data Engineering — ekstraksi dataset, cleaning, augmentasi gambar |
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| APC414D6Y0138 | **Luhung Pandyaska Suyi** | Machine Learning | Model Architecture & Training — CNN, hyperparameter tuning |
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| APC013D6Y0269 | **Ardian** | Machine Learning | Model Evaluation & Deployment Prep — confusion matrix, konversi ke production-ready |
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---
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## 📋 Ringkasan Eksekutif
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### Masalah
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Data BPS menunjukkan penurunan luas panen jagung dari **2.764.366 Ha (2022)** menjadi **2.487.191 Ha (2023)**. Salah satu penyebab utamanya adalah penyakit daun seperti **Hawar Daun**, **Karat Daun**, dan **Bercak Daun Abu-abu** yang menyebabkan nekrosis dan menghambat fotosintesis.
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Petani sering kesulitan mengidentifikasi penyakit secara kasat mata dan memiliki **ketergantungan tinggi pada POPT** (Petugas Pengendali Organisme Pengganggu Tumbuhan) akibat minimnya media pembelajaran interaktif.
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### Solusi
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ZeaVis Edu menggunakan **Computer Vision** sebagai asisten edukasi interaktif:
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1. 📸 **Unggah** foto daun jagung yang diduga terinfeksi
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2. 🤖 **Deteksi otomatis** penyakit oleh model AI (EfficientNetV2B0)
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3. 📚 **Dapatkan** informasi detail penyakit, panduan pencegahan, dan rekomendasi obat secara mandiri
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### Metode Teknis
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| Komponen | Pilihan |
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| Arsitektur Model | **EfficientNetV2B0** — keseimbangan optimal antara akurasi dan efisiensi parameter |
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| Metode Pelatihan | **Transfer Learning** pada Google Colab (GPU T4) |
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| Sumber Dataset | Kaggle — [Corn Leaf Disease](https://www.kaggle.com/datasets/ndisan/corn-leaf-disease) |
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| Deployment | VPS dengan Docker, ONNX Runtime untuk inferensi real-time |
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---
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## 🎯 Cakupan & Deliverables
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### Cakupan
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| ✅ Dalam Cakupan | ❌ Di Luar Cakupan |
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| Klasifikasi 3 penyakit + 1 daun sehat | Penyakit pada batang atau buah jagung |
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| Deteksi berbasis unggah gambar daun | Prediksi tanpa input gambar |
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| Rekomendasi obat & penanganan | Diagnosis pengganti ahli/POPT |
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| Aplikasi web edukatif | Aplikasi mobile native |
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### 4 Kelas yang Diklasifikasikan
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| Label | Nama Ilmiah | Gejala |
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|---|---|---|
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| **Hawar Daun** | *Northern/Southern Leaf Blight* | Hawar coklat memanjang pada daun |
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| **Karat Daun** | *Common Rust* | Bintik coklat kemerahan berbentuk pustula |
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| **Bercak Daun** | *Gray Leaf Spot* | Bercak abu-abu memanjang |
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| **Daun Sehat** | — | Tanpa gejala penyakit |
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### Deliverables Proyek
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| No | Tahapan | Deskripsi |
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|---|---|---|
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| 1 | **Pengumpulan Data** | Dataset gambar 3 penyakit + 1 daun sehat dari Kaggle beserta pelabelan |
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| 2 | **Model ML** | Model Computer Vision terlatih di Google Colab, siap produksi |
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| 3 | **UI Antarmuka** | Front-End berbasis React + Vite dengan fitur unggah gambar |
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| 4 | **Back-End Integration** | API + ML Service untuk inferensi real-time via Docker |
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| 5 | **Prototipe Akhir** | Aplikasi web final dengan klasifikasi + modul edukasi (rekomendasi obat & penanganan) |
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---
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## 📅 Jadwal
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| Minggu | Tanggal | Fase | Aktivitas |
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|---|---|---|---|
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| **1** | 11–17 Mei 2026 | Inisiasi & Data | Spesifikasi teknis (Asep) • Dataset dari Kaggle + preprocessing (Taufik) • Wireframe UI/UX (Selly) |
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| **2** | 18–24 Mei 2026 | Training & Dev Awal | Implementasi EfficientNetV2B0 di Colab (Luhung) • Slicing UI ke React (Selly) • Setup server, database, routing API (Asep) |
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| **3** | 25–31 Mei 2026 | Evaluasi & Modul Edukasi | Evaluasi akurasi + konversi model ke ONNX/TFLite (Ardian) • Halaman edukasi obat & penanganan (Selly) • RESTful API untuk image upload & inferensi (Asep) |
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| **4** | 1–7 Juni 2026 | Integrasi & Testing | Integrasi penuh Front-End ↔ API ↔ Model ML • Pengujian end-to-end • Stress testing & error handling (Semua) |
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| **5** | 8–14 Juni 2026 | Deployment & Finalisasi | Deployment ke VPS (Asep) • Bug fixing & optimalisasi UI/UX (Selly) • Dokumentasi teknis & materi presentasi (Semua) |
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---
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## ⚠️ Manajemen Risiko
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| Risiko | Solusi |
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| **Overfitting akibat imbalanced data** | Augmentasi tingkat lanjut (kecerahan, noise, rotasi) + confidence threshold < 75% → minta user foto ulang |
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| **Server downtime / latensi tinggi** | Batasan upload ≤ 5 MB + kompresi server-side + rate limiting + container Docker isolasi resource |
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| **Foto blur / objek bukan daun jagung** | Panduan visual (overlay) pada UI + validasi anomali + disclaimer "alat bantu edukasi, bukan pengganti POPT" |
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| **Bottleneck integrasi ML ↔ API ↔ UI** | API Contract ketat di minggu ke-1 + integrasi bertahap (CI) mulai minggu ke-3 |
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---
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## 🏗️ Arsitektur Proyek
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```
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.
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├── apps/
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│ ├── api/ # Backend Elysia/Bun + Drizzle ORM + PostgreSQL
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│ ├── ml-service/ # Rust/Axum + ONNX Runtime inference service
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│ └── web/ # Frontend React + Vite + Tailwind CSS
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├── Machine_Learning/ # Pipeline dataset, training Colab, ekspor model
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│ └── README.md # ⤷ Panduan lengkap pipeline ML
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├── infra/
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│ └── README.md # ⤷ Panduan deployment multi-VPS
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├── packages/shared/ # Tipe & utilitas TypeScript bersama
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├── telemetry/ # Submodule — Prometheus → ClickHouse pipeline
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├── docker-compose.yml # Konfigurasi deployment container
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├── package.json # Root workspace Bun + Moon
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└── README.md # ⤷ Anda di sini
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```
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| Komponen | Teknologi | Dokumentasi |
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| Web Frontend | React, Vite, Tailwind, Zustand, TanStack Query | `apps/web/` |
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| API Backend | Bun, Elysia, Drizzle ORM, PostgreSQL | `apps/api/` |
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| ML Inference | Rust, Axum, ONNX Runtime | [`apps/ml-service/README.md`](apps/ml-service/README.md) |
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| ML Pipeline | Python, TensorFlow/Keras, EfficientNetV2B0 | [`Machine_Learning/README.md`](Machine_Learning/README.md) |
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| Infrastruktur | Docker, Coolify, Traefik, Tailscale | [`infra/README.md`](infra/README.md) |
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| Telemetry | Prometheus, ClickHouse, Vector, Vue 3 | `telemetry/` |
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---
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## 🛠️ Tech Stack
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### Frontend
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React • Vite • TypeScript • React Router • TanStack Query • Zustand • Tailwind CSS
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### Backend API
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Bun • Elysia • Drizzle ORM • PostgreSQL • prom-client
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### Machine Learning
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Python • TensorFlow/Keras • EfficientNetV2B0 • Google Colab (GPU T4)
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Rust • Axum • ONNX Runtime • TFLite • TensorFlow.js
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### DevOps & Infrastruktur
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Docker • Docker Compose • Coolify • Traefik • Tailscale • GitHub Actions (CI/CD)
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### Observabilitas
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Prometheus • Metric Ingester (Go) • Vector • ClickHouse • Query Proxy (Go) • Telemetry UI (Vue 3)
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---
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## 🚀 Memulai
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### Prasyarat
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- **Bun** — runtime & package manager
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- **Python 3.9–3.11** — pipeline ML
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- **Rust & Cargo** — `apps/ml-service`
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- **Docker & Docker Compose** — deployment & telemetry
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- **PostgreSQL** — backend API
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### Instalasi
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```bash
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git clone https://github.com/ATLAS-PJK-GM007/ZeaVis-Edu.git
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cd ZeaVis-Edu
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bun install
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```
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### Menjalankan Development
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```bash
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bun run dev # Semua service (web + api)
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cd apps/web && bun run dev # Hanya frontend
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cd apps/api && bun run start # Hanya backend API
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cd apps/ml-service && cargo run # Hanya ML service (port 8000)
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```
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### Environment Variables
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Salin `.env.example` ke `.env` dan isi:
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| Variable | Keterangan |
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| `DATABASE_URL` | URL koneksi PostgreSQL |
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| `SESSION_SECRET` | Secret untuk session auth |
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| `WEB_APP_URL` | URL frontend (untuk CORS) |
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| `ML_SERVICE_URL` | URL layanan inferensi ML |
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### Pipeline ML (Ringkasan)
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1. Unduh 3 dataset ZIP → letakkan di `Machine_Learning/`
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2. `python preprocessing.py` — gabungkan & bersihkan dataset
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3. Upload `dataset.zip` ke Google Drive
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4. Jalankan `notebook.ipynb` di Google Colab (GPU T4)
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5. Download `best_model.keras`
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6. `python save_model.py` → TFLite + SavedModel
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7. Konversi ke TFJS & ONNX
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> 📖 **Panduan lengkap:** [`Machine_Learning/README.md`](Machine_Learning/README.md)
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### Deployment
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```bash
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docker compose up -d # App services
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make telemetry-up # Telemetry stack
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```
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> 📖 **Panduan infrastruktur:** [`infra/README.md`](infra/README.md)
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---
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## 📚 Dokumentasi
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| Dokumen | Isi |
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| [`Machine_Learning/README.md`](Machine_Learning/README.md) | Pipeline ML lengkap — preprocessing, training Colab, ekspor TFLite/TFJS/ONNX |
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| [`apps/ml-service/README.md`](apps/ml-service/README.md) | ML Inference Service — setup, endpoint API, konfigurasi |
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| [`infra/README.md`](infra/README.md) | Arsitektur multi-VPS — diagram, GitHub Secrets, port, metrics flow |
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| [`METRICS.md`](METRICS.md) | Daftar lengkap metrik Prometheus |
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| `telemetry/` (submodule) | Source code telemetry stack |
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---
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## 🔧 Troubleshooting
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| Masalah | Solusi |
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| `bun install` gagal | `bun --version` — pastikan ≥ 1.x |
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| API perlu database | Isi `DATABASE_URL` di root `.env` |
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| ML service gagal muat model | `ls Machine_Learning/model/model.onnx` — jalankan pipeline ML jika belum ada |
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| Docker Compose gagal | `docker network create app-shared-net` |
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| Konversi TFJS gagal | `export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python` |
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---
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## 📖 Daftar Pustaka
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1. Prayogi, A. et al. *"Klasifikasi Penyakit Daun Jagung Menggunakan CNN"* — [SISTEMATIS](https://ejournal.rizaniamedia.com/index.php/sistematis/article/view/87/49)
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2. Nugroho, A. et al. *"Deteksi Penyakit Daun Jagung dengan Deep Learning"* — [MIND Journal](https://ejurnal.itenas.ac.id/index.php/mindjournal/article/view/14032/4209)
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3. Ramadhan, F. et al. *"Identifikasi Penyakit Jagung Berbasis Citra Digital"* — [Informa](https://www.informa.poltekindonusa.ac.id/index.php/informa/article/view/199/170)
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4. Corteva Agriscience. *"Kenali Ragam Jenis Penyakit Jagung dan Cara Mengatasinya"* — [corteva.com](https://www.corteva.com/id/berita/Kenali-Ragam-Jenis-Penyakit-Jagung-dan-Cara-Mengatasinya.html)
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---
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<p align="center">
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<sub>
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Capstone Project • Pijak × IBM SkillsBuild • AI for Smart Education<br>
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© 2026 ZeaVis Edu Team
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</sub>
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</p>
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