Machine Learning Models to Forecast Rice Prices at the Milling Level According to Quality

Andika  Putri Ratnasari; Kismiantini

Authors

Andika Putri Ratnasari Statistics Study Program, Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, Yogyakarta State University, Indonesia
Kismiantini Statistics Study Program, Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, Yogyakarta State University, Indonesia

Keywords:

Machine learning, nonlinear, time series, forecasting, accuracy

Abstract

Machine learning methods have the ability to model nonlinear time series data. Support vector regression (SVR) and double random forest (DRF) are supervised learning techniques that can be applied to such data. Since a comparative analysis of SVR and DRF for rice price forecasting has not been conducted in previous studies, this research aims to compare the performance of these two models in analyzing nonlinear rice price dynamics. The dataset consists of monthly milling-level rice prices for premium, medium, and out-of-quality categories, all of which exhibit nonlinear characteristics. Model performance was evaluated using three forecasting accuracy metrics: mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean square error (RMSE). The results show that the MAPE values for all models are below 10%, indicating high predictive accuracy. Across all evaluation metrics, the SVR model consistently outperformed the DRF model. Therefore, SVR was selected as the best model to generate forecasts for premium, medium, and out of quality rice prices.

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