Enhancing Paddy Production in Malaysia: A Comparative Analysis of Multiple Regression with External Factors

Rice is a basic food that is consumed by almost half of the world's population, especially in Malaysia. Unfortunately, paddy productivity has recently decreased dramatically, which has hampered local rice supply and forced Malaysia to depend on rice from neighbouring nations. Therefore, the modelling of paddy production is important because it provides an insight that ensure a sufficient supply of a locally produced paddy. In this study, several multiple regression models were used to predict paddy production model in Malaysia from 1980 to 2022. The multiple regression models are then compared, and the best model is determined. The regression model used in this study are Multiple Linear Regression (MLR), Multivariate Adaptive Regression Spline (MARS) and Support Vector Regression (SVR). The developed models will be evaluated using RMSE, MAPE and Ljung-Box Test. These are excellent tools for gauging the precision of the fitted model, thus can be used to evaluate the model. Based on the study, the MARS model is better at modelling Malaysian paddy production from 1980 to 2022 since it has the smallest number of measurement errors. Therefore, MARS model is the best regression model to be used to model paddy production in Malaysia compared to MLR and SVR model. Since all the models have autocorrelation, a more effective approach and model can be presented to overcome the autocorrelation issue in the future.