Modeling and prediction of wetland rice production using support vector regression
Muhammad Alkaff, Husnul Khatimi, Wenny Puspita, Yuslena Sari
Food security is still one of the main issues faced by Indonesia due to its large population. Rice as a staple food in Indonesia has experienced a decline in production caused by unpredictable climate change. In dealing with climate change, adaptation to fluctuating rice productivity must be made. This study aims to build a prediction model of wetland rice production on climate change in South Kalimantan Province which is one of the national rice granary province and the number one rice producer in Kalimantan Island. This study uses monthly climatic data from Syamsudin Noor Meteorological Station and quarterly wetland rice production data from Central Bureau of Statistics of South Kalimantan. In this research, Support Vector Regression (SVR) method is used to model the effect of climate change on wetland rice production in South Kalimantan. The model is then used to predict the amount of wetland rice production in South Kalimantan. The results showed that the prediction model with the RBF kernel with the parameter of C = 1.0, epsilon = 0.002 and gamma = 0.2 produces good results with the RMSE value of 0.1392.
Indonesia; prediction; support vector regression; wetland rice