Prediction of Indonesian Inflation Rate Using Regression Model Based on Genetic Algorithms
DOI:
https://doi.org/10.15575/join.v5i1.532Keywords:
Genetic Algorithm, Inflation Level, Mean Squared Error, Prediction, RegressionAbstract
Inflation occurs where there is an increase in the price of goods or services in general and continuously in a country. Uncontrolled inflation will have an impact on the decline of the Indonesian economy. Therefore, the prediction of future inflation levels is necessary for the government to develop economic policies in the future. Prediction of inflation levels can be done by studying historical past Consumer Price Index (CPI) data. Regression methods are often used to solve prediction problems. The problem of finding the optimal prediction model can be seen as an optimization problem. Genetic algorithms are often used to deal with optimization problems. Thus, this work proposed to use a genetic algorithm-based regression model for predicting inflation levels. The model was trained and evaluated using real CPI data which obtained from the Indonesian Central Bank. Based on the experiment, it is proved that the proposed model is effective in predicting the inflation level as it gains MSE of 0.1099.
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