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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Yessica Siagian
Universitas Royal
Computer Science & IT Engineering

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Comparison Of Machine Learning Algorithms For Rice Production Prediction Abdul Karim; Yuwaldi Away; Syahrial; Roslidar; Jeperson Hutahaean; William Ramdhan; Yessica Siagian
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April - In progress
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7453

Abstract

Rice production forecasting plays an important role in supporting future agricultural planning, food supply management, and food security. Accurate yield prediction allows governments and farmers to estimate production outcomes and develop appropriate strategies to maintain stable food availability.This study addresses this gap by comparing four regression-based machine learning models: Random Forest, XGBoost, Support Vector Regression (SVR), and Artificial Neural Network (ANN). All models were trained and tested using the same dataset to ensure a fair evaluation. Model performance was measured using the coefficient of determination (R²). The results show that Random Forest achieved the best performance (R² = 0.963), followed by XGBoost (R² = 0.959). In contrast, SVR (R² = -0.064) and ANN (R² = -2.417) performed poorly, indicating limited predictive capability. Overall, these findings suggest that ensemble-based methods, particularly Random Forest and XGBoost, are more reliable and effective for rice production forecasting compared to SVR and ANN.
Co-Authors Abdul Karim Jeperson Hutahaean Roslidar Syahrial, Syahrial William Ramdhan Yuwaldi Away