An Inflation Rate Prediction Based on Backpropagation Neural Network Algorithm

(1) Purnawansyah Purnawansyah Mail (Universitas Muslim Indonesia, Indonesia)
(2) * Haviluddin Haviluddin Mail (Scopus ID: 56596793000; Departement Ilmu Komputer; Universitas Mulawarman, Indonesia)
(3) Hario Jati Setyadi Mail (Fakultas Ilmu Komputer dan Teknologi Informasi Universitas Mulawarman, Indonesia)
(4) Kelvin Wong Mail (Fakultas Ilmu Komputer dan Teknologi Informasi Universitas Mulawarman)
(5) Rayner Alfred Mail (Universiti Malaysia Sabah, Malaysia)
*corresponding author


This article aims to predict the inflation rate in Samarinda, East Kalimantan by implementing an intelligent algorithm, Backpropagation Neural Network (BPNN). The inflation rate data was obtained from the Provincial Statistics Bureau of Samarinda for the period January 2012 to January 2017. The method used to measure accuracy algorithm prediction was the mean square error (MSE). Based on the experiment results, the BPNN method with architectural parameters of 5-5-5-1; the learning function was trainlm; the activation functions were logsig and purelin; the learning rate was 0.1 and able to produce a good level of prediction error with an MSE value of 0.00000424. The results showed that the BPNN algorithm can be used as an alternative method in predicting inflation rates in order to support sustainable economic growth, so that it can improve the welfare of the people in Samarinda, East Kalimantan.


BPNN; MSE; Prediction; Inflation Rates; Economic



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