Comparative Evaluation of Long Short-Term Memory and Support Vector Regression for Daily Rice Price Forecasting in Makassar

Arwini Arisandi (Politeknik Pertanian Negeri Pangkajene Kepulauan)
Ismail Gaffar (Politeknik Pertanian Negeri Pangkajene Kepulauan)
Sitti Nurhaliza (Universitas Tadulako)
Hardianti Hafid (Universitas Negeri Makassar)

Journal of Mathematics, Computation and Statistics (JMATHCOS)
Vol. 9 No. 3 (2026): Volume 09 Issue 03 (September 2026)

Rice is a strategic food commodity that plays an important role in food security, household purchasing power, and regional economic stability. Fluctuations in daily rice prices can affect inflation, market behavior, and policy decisions related to food distribution and price stabilization. This study aims to comparatively evaluate the performance of Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) models in forecasting daily rice prices, particularly for Premium Rice and Medium Rice. The study initially used 1,077 observations of daily rice price time-series data. Following preprocessing, including the construction of lag and moving-average features, 1,047 observations remained for modeling. Descriptive analysis showed that Premium Rice had a minimum price of IDR 11,000, a maximum price of IDR 16,250, a mean price of IDR 13,690.55, and a standard deviation of 1,300.812. Meanwhile, Medium Rice had a minimum price of IDR 9,800, a maximum price of IDR 14,760, a mean price of IDR 12,212.12, and a standard deviation of 1,334.867. The LSTM model was implemented using Python, whereas the SVR model was implemented using R. Both models were evaluated using the same chronological data partitions and testing period. Model performance was assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Both models demonstrated accurate forecasting performance, with MAPE values below 10%. Under the experimental configuration and common testing period used in this study, SVR achieved lower forecasting errors than LSTM for both rice categories. For Premium Rice, SVR produced an MAE of 61.18, an RMSE of 159.78, and a MAPE of 0.42%, whereas LSTM produced an MAE of 180.00, an RMSE of 293.00, and a MAPE of 1.18%. For Medium Rice, SVR produced an MAE of 67.03, an RMSE of 209.29, and a MAPE of 0.49%, compared with LSTM, which produced an MAE of 181.00, an RMSE of 284.00, and a MAPE of 1.32%. These findings indicate that SVR was more suitable for the data and feature configuration applied in this study.