> NAMA KELOMPOK   : Kelompok 13
> NAMA SUPERVISOR :
    - SAKTI ARISENA DAMAI PRASETYO (2042221029)
    - YASMINE YULIANA SALIM (2042221062)
    - AULIAZQI RARASATI NAJID (2042221116)

> NAMA DEPARTEMEN / INSTITUT : Teknik Instrumentasi / INSTITUT TEKNOLOGI SEPULUH NOPEMBER

🌾 Rice Classification with Multi-Layer Neural Network in Rust

Proyek ini merupakan implementasi sederhana dari Multi-Layer Neural Network (MLP) untuk melakukan klasifikasi varietas beras berdasarkan fitur morfologi seperti solidity, aspect ratio, roundness, dan compactness. Proyek ini ditulis dalam bahasa pemrograman Rust menggunakan ndarray dan linfa.

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📂 Dataset

Dataset yang digunakan adalah file CSV bernama Rice_MSC_Dataset_sample.csv, dengan kolom-kolom:

• Solidity
• Aspect_Ratio
• Roundness
• Compactness
• Class (label kelas, seperti "Jasmine", "Karacadag", dll.)

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🧠 Arsitektur Neural Network

Model neural network yang digunakan terdiri dari:

• 4 input neurons (sesuai jumlah fitur)
• 1 hidden layer dengan 64 neurons dan aktivasi ReLU
• Output layer sesuai jumlah kelas (one-hot encoding)
• Optimisasi menggunakan gradient descent manual

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🚀 Fitur Program

• Membaca dataset dari file .csv
• Mengubah label kelas menjadi indeks numerik menggunakan one-hot encoding
• Melatih neural network dengan forward dan backward propagation
• Menampilkan akurasi prediksi dan hasil klasifikasi
• Progres pelatihan ditampilkan dengan animasi titik berjalan

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📦 Dependencies

Tambahkan dependencies berikut di Cargo.toml:

[dependencies]
linfa = "0.7.1"
linfa-svm = "0.7.2"
linfa-nn = "0.7.1"
linfa-clustering = "0.7.1"
ndarray = "0.15.6"
csv = "1.1"
rand = "0.9.0"
plotters = "0.3.0"
linfa-logistic = "0.7.0"

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🛠️ Cara Menjalankan

1. Pastikan Anda memiliki Rust terinstal. Jika belum:

   curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

2. Clone repositori ini:

   git clone https://github.com/username/rice-nn-rust.git
   cd rice-nn-rust

3. Letakkan file Rice_MSC_Dataset_sample.csv ke dalam folder data/.

4. Jalankan program:

   cargo run

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📊 Output

• Menampilkan fitur dan peta kelas
• Menampilkan akurasi dari model
• Contoh prediksi:

Sample 0: Predicted 2, Actual 2
Sample 1: Predicted 0, Actual 0
...
Accuracy: 96.67%

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Sumber Refrensi :

• https://www.kaggle.com/code/satyaprakashshukl/rice-classification

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