Inflation Rate Prediction in Indonesia using Optimized Support Vector Regression Model


Irvi Oktanisa, Wayan Firdaus Mahmudy, Ghozali Maski


Inflation is a indicator which illustrated the economics condition of a country. This moneter phenomenom is signed with the increase of price in entire case. It can cause an effect for political sector which impact to economic stability in a nation. The importance of inflation control is very important due to the high and unstable of inflation will cause negative impact  to economic and social in society.  One of the solutions to control the inflation rate is predicting the inflation rate. This research using SVR as machine learning that is being optimized by GA as evolutionary agorithm as predicting method. SVR can solve nonlinear regression problems to linear regression using Kernel function that easy to implement. But, in SVR there is no general rule to set the parameters of SVR. Therefore, this research proposed to use GA to optimize the parameters of SVR. GA can solve the optimization problems in various research of economics prediction problem. Based on the testing that has been conducted, GA-SVR generate the MSE value is 0.03767, lower than SVR basic method is 0.053158. It proves that GA-SVR method can be utilized for predicting.

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