Forecasting of Rice Harvest Results Using SVR Modeling Techniques - Anamisa, Devie Rosa; Khotimah, Bain Khusnul; Hariyawan, Mohammad Yanuar; Irhamni, Firli; Jauhari, Achmad; Mufarroha, Fifin Ayu; Violina, Dina; Nuraini, Dinah (Department of Mathematics, Universitas Negeri Gorontalo, 2025)
Forecasting is an activity that predicts future values by utilizing existing track record data. The object of this study is rice plants because they are the primary food source for the Indonesian people. Every year, the Government strives for rice farmers throughout Indonesia to produce abundant rice harvests to meet the community's food needs. Therefore, rice farmers need a system that can predict their rice harvests to obtain information about future harvests to find out whether their harvests have decreased or increased so that they can determine efforts that can be made in the future and can be used as a policy maker for the Government in maintaining the national food security chain. This study uses time series data on rice harvests in Pamekasan, Madura, for 2007-2023 using the Support Vector Regression (SVR) model. The results of several trials have shown that the application of the SVR model for forecasting rice harvests in 2024 has produced good accuracy with a relatively low MAPE error rate of 3.97%, and the rice harvest has reached an average prediction of 15470.08 tons with an average actual data of 7937.884 tons. Therefore, applying this SVR model can be recommended for predicting future rice harvests.