Prediction of Rice Production to Support Food Security in Bogor Regency using Linear Regression and Support Vector Machine (SVM)

Abstract

A prediction is an estimation of something that has not yet occurred. Its purpose is to minimize uncertainty and reduce errors in planning. Bogor Regency, with the largest population in West Java, requires a substantial amount of food. Rice production must meet the consumption needs of the population. To anticipate potential rice shortages, effective planning, and reduced dependence on rice imports, research is needed to predict rice production. This study aims to predict rice production using Linear Regression and Support Vector Machine (SVM) algorithms. Secondary data from the Department of Food Crops and Horticulture, and the Central Statistics Agency (BPS) of Bogor Regency were utilized. Results show that the Linear Regression method outperformed SVM, with MSE 236202.323, RMSE 486.007, MAE 388.712, and R2 1.000. In contrast, SVM yielded MSE 1461472466.751, RMSE 38229.2.10, MAE 303333.535, and R2 -0.065. In conclusion, the prediction using Linear Regression demonstrated better accuracy than SVM.
 Keywords: Prediction, Algorithm, SVM. Linear Regression.