One aspect of food security that needs to be improved in Indonesia is the resilience of natural resources, namely how the Indonesian government takes countermeasures when weather anomalies occur. This action is noteworthy considering that Indonesia is vulnerable to climate change, so the impact could negatively affect the agricultural industry in Indonesia, which results in food security, especially in the rice commodity. Increasing rice prices can harm the economy and the welfare of society. In solving this problem, an analysis of the impact of weather factors on rice prices will be carried out, and then a rice price model will be created using rice price data and past weather factors that can capture movements and changes in rice prices. Various methods have been developed to forecast agricultural commodity prices, from traditional to intelligent forecasting methods and from single to hybrid models. This study will compare traditional forecasting methods using the ARIMAX-GARCH method and the intelligent forecasting method using the support vector regression (SVR) method to forecast rice prices and then compare the performance of the two models. The correlation results obtained in the mathematical regression modeling show that rainfall tends to have a positive effect on rice prices. The results of a comparison of forecasting methods are that each method has its advantages, and the SVR method is superior in detecting price movements. In contrast, the ARIMAX-GARCH method is superior if the predicted data is constant, but overall the SVR method is superior.