> 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
-
Membaca dataset dari file Excel (
.xlsx). -
Melatih model:
- SVM (Support Vector Machine) untuk regresi.
- K-Means Clustering untuk melakukan pengelompokan.
-
Menampilkan data asli, hasil prediksi SVM, dan cluster K-Means pada terminal.
-
Menyimpan hasil visualisasi dalam bentuk file gambar (
plot.png). -
Menampilkan progress training K-Means dengan animasi sederhana di terminal.
Pastikan Anda telah menginstal:
- Rust (https://www.rust-lang.org/)
- File dataset:
Rice_MSC_Dataset_sample.xlsx
Berikut adalah crates yang digunakan pada project ini:
[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"
calamine = "0.19"git clone https://github.com/username/project-name.git
cd project-namePastikan file Rice_MSC_Dataset_sample.xlsx berada di direktori root proyek.
cargo run-
Membaca Dataset:
-
Program membaca file Excel (
Rice_MSC_Dataset_sample.xlsx) dari sheetSheet1. -
Field yang digunakan:
ASPECT_RATIO(fitur/input)COMPACTNESS(label/target)
-
-
Pra-pemrosesan Data:
- Dataset di-split menjadi data training (80%) dan testing (20%).
-
Training Model:
-
SVM (Support Vector Machine)
- Menggunakan linear kernel untuk memodelkan hubungan antara
ASPECT_RATIOdanCOMPACTNESS.
- Menggunakan linear kernel untuk memodelkan hubungan antara
-
K-Means Clustering
- Mengelompokkan data ke dalam 3 cluster.
- Menampilkan progress training dengan animasi sederhana.
-
-
Prediksi & Output:
- Menampilkan prediksi SVM dan cluster K-Means pada data testing di terminal.
-
Visualisasi Data:
-
Membuat plot
plot.pngyang berisi:- Data asli (warna biru)
- Hasil prediksi SVM (warna merah)
- Cluster K-Means (warna hijau, magenta, cyan)
-
=== Data Asli ===
No ASPECT_RATIO (Fitur) COMPACTNESS (Label)
------------------------------------------------
1 2.3450 0.6500
2 3.1200 0.7000
...
=== Training SVM ===
=== Hasil Prediksi SVM ===
Data Test Prediksi
--------------------------
2.3450 0.6450
...
=== Training K-Means ===
Training K-Means...
Training K-Means selesai!
=== Hasil Prediksi K-Means ===
Data Test Cluster
--------------------------
2.3450 1
...
Grafik telah disimpan sebagai plot.png
project-name/
│
├── Cargo.toml
├── src/
│ └── main.rs
├── Rice_MSC_Dataset_sample.xlsx
└── plot.png (setelah program dijalankan)
-
Pastikan dataset dengan header yang sesuai:
- Kolom ke-2 (
index 1):ASPECT_RATIO - Kolom ke-4 (
index 3):COMPACTNESS
- Kolom ke-2 (
-
Plot akan otomatis digenerate setelah proses training dan prediksi selesai.
Sumber Refrensi :
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[5] S. Ibrahim, S. B. A. Kamaruddin, A. Zabidi, and N. A. M. Ghani, “Contrastive analysis of rice grain classification techniques: Multi-class support vector machine vs artificial neural network,” IAES Int. J. Artif. Intell., vol. 9, no. 4, pp. 616–622, 2020.
[6] A. S. Hamzah and A. Mohamed, “Classification of white rice grain quality using ann: A review,” IAES Int. J. Artif. Intell., vol. 9, no. 4, pp. 600–608, 2020.
[7] MUH ZAINAL ALTIM, FAISAL, SALMIAH, KASMAN, ANDI YUDHISTIRA, and RITA AMALIA SYAMSU, “Pengklasifikasi Beras Menggunakan Metode Cnn (Convolutional Neural Network),” J. INSTEK (Informatika Sains dan Teknol., vol. 7, no. 1, pp. 151–155, 2022.
[8] P. S. Sampaio, A. S. Almeida, and C. M. Brites, “Use of artificial neural network model for rice quality prediction based on grain physical parameters,” Foods, vol. 10, no. 12, 2021.
[9] W. Xia, R. Peng, H. Chu, X. Zhu, Z. Yang, and ..., “An Overall Real-Time Mechanism for Classification and Quality Evaluation of Rice,” Available SSRN ….
[10] A. Bhattacharjee, K. R. Singh, T. S. Singh, S. Datta, U. R. Singh, and G. Thingbaijam, “INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING A Comparative Study on Rice Grain Classification Using Convolutional Neural Network and Other Machine Learning Techniques,” pp. 0–1, 2024.
[11] T. T. H. Phan, Q. T. Vo, and H. Du Nguyen, “A novel method for identifying rice seed purity using hybrid machine learning algorithms,” Heliyon, vol. 10, no. 14, 2024.
[12] Y. Wang, H. Wang, and Z. Peng, “Rice diseases detection and classification using attention based neural network and bayesian optimization,” Expert Syst. Appl., vol. 178, 2021.
[13] Y. Haddad, K. Salonitis, and C. Emmanouilidis, “A decision-making framework for the design of local production networks under largescale disruptions,” Procedia Manuf., vol. 55, no. C, pp. 393–400, 2021.
[14] I. Samarakoon and P. Liyanage, “Impact of Data Analytics on Operations Success of Apparel Sector ABC Clothing Pvt Limited (Sri Lanka),” Int. J. Comput. Appl., vol. 184, no. 33, pp. 1–15, 2022.
[15] Q. W. Kong, J. He, Z. W. Zhang, H. Zheng, and P. Z. Wang, “Projection as a way of thinking to find factors in factor space,” Procedia Comput. Sci., vol. 199, pp. 503–508,