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Kelvin Wong
Fakultas Ilmu Komputer dan Teknologi Informasi Universitas Mulawarman
Computer Science & IT Electrical & Electronics Engineering

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An Inflation Rate Prediction Based on Backpropagation Neural Network Algorithm Purnawansyah Purnawansyah; Haviluddin Haviluddin; Hario Jati Setyadi; Kelvin Wong; Rayner Alfred
International Journal of Artificial Intelligence Research Vol 3, No 2 (2019): December 2019
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1806.11 KB) | DOI: 10.29099/ijair.v3i2.112

Abstract

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.
Co-Authors Haviluddin Haviluddin Purnawansyah Purnawansyah Rayner Alfred Setyadi, Hario Jati