Comparison Of Radial Basis Function Neural Networks (RBFNN) And Autoregressive Moving Average (ARMA) Algorithms On Inflation Rate Prediction Models In Batam City
DOI:
https://doi.org/10.54209/infosains.v14i02.4713Keywords:
RBFNN, ARMA, Time Series, InflationAbstract
The inflation rate in the city of Batam from January 2023 to April 2024 continues to fluctuate, so an accurate prediction model is needed so that inflation control can be carried out optimally. In this study, we conducted a comparative analysis between the Radial Basis Function Neural Network (RBFNN) method and the Autoregressive Moving Average (ARMA) model in predicting the inflation rate. The data used is historical data on the inflation rate of Batam City from January 2009 to April 2024. The results of the analysis show that the RBFN method with an MSE value of 0.239 is able to provide a more accurate prediction compared to the ARMA model (2.3) with an MSE value of 0.246 in predicting the inflation rate in Batam City. This is due to the RBFN's ability to capture complex and non-linear patterns contained in inflation data. In addition, the performance of RBFNN is also affected by the number of neurons and the basis function used. Thus, the results of this study show that the RBFN method can be an effective and efficient alternative in predicting the inflation rate in Batam City.
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