An Inflation Rate Prediction Based on Backpropagation Neural Network Algorithm

Purnawansyah Purnawansyah (Universitas Muslim Indonesia)
Haviluddin Haviluddin (Scopus ID: 56596793000 Departement Ilmu Komputer Universitas Mulawarman)
Hario Jati Setyadi (Fakultas Ilmu Komputer dan Teknologi Informasi Universitas Mulawarman)
Kelvin Wong (Fakultas Ilmu Komputer dan Teknologi Informasi Universitas Mulawarman)
Rayner Alfred (Universiti Malaysia Sabah)

International Journal of Artificial Intelligence Research
Vol 3, No 2 (2019): December 2019

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 https://samarindakota.bps.go.id/ 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.