Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Roslidar
Unknown Affiliation
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Comparison Of Machine Learning Algorithms For Rice Production Prediction In Sumatra Abdul Karim; Yuwaldi Away; Syahrial; Roslidar; Jeperson Hutahaean; William Ramdhan; Yessica Siagian
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Rice production prediction is a crucial aspect in agricultural planning and food security. This study compares the performance of four regression algorithms in predicting rice production based on agronomic and climatological variables. The algorithms used are Random Forest Regression, XGBoost Regression, Support Vector Regression (SVR), and Artificial Neural Network (ANN). The evaluation results showed that Random Forest performed best with an R² of 0.963, followed by XGBoost with an R² of 0.959, indicating that these two models were able to explain more than 95% of the data variation. In contrast, SVR performed poorly with an R² of -0.064, while ANN had the worst result with an R² of -2.417, indicating the model's unsuitability for the dataset used. Thus, it can be concluded that Random Forest and XGBoost are the best options for rice production prediction, while SVR and ANN require further optimization to be used effectively in this context.
Co-Authors Abdul Karim Jeperson Hutahaean Syahrial William Ramdhan Yessica Siagian Yuwaldi Away