Forecasting Average Rice Prices at Milling Level According to Quality Using Support Vector Regression
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https://doi.org/10.36456/jstat.vol17.no1.a9245Keywords:
Forecasting, Support Vector Regression, Average Rice PricesAbstract
Indonesia is an agricultural country where the majority of the population work as farmers and one of the humongous commodities produced is rice. Rice is a very important commodity for the Indonesian people, because it is the main food of them. This is why rice production in Indonesia is the big concern to the government, including of the average rice prices at milling level. The fluctuative of the rice prices will be affect to the purchasing power of the people. One of the efforts that can be made to prepare a policy to increase people's purchasing power of the rice is by forecasting. This study used SVR to modeling the average rice prices using 114 datasets obtained from January 2013 to June 2023, then evaluating its performance using Mean Absoute Percetage Error (MAPE). The best model formed from a linear kernel with parameters ε = 0.078 and C = 3.1. The model produced the smallest MAPE value of 2.32% in testing data and 1.2% in training data which also less than 10% meaning that the performance of the model to forecast the average price of rice is very high.
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