An Inflation Rate Prediction Based on Backpropagation Neural Network Algorithm

International Journal of artificial intelligence research

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Title An Inflation Rate Prediction Based on Backpropagation Neural Network Algorithm
Creator Purnawansyah, Purnawansyah
Haviluddin, Haviluddin
Setyadi, Hario Jati
Wong, Kelvin
Alfred, Rayner
Subject artificial intelligence; machine learning; prediction
BPNN; MSE; Prediction; Inflation Rates; Economic
Description 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.
Publisher STMIK Dharma Wacana
Contributor Universitas Mulawarman
Date 2019-12-30
Type info:eu-repo/semantics/article
Peer-reviewed Article
Format application/pdf
Source International Journal of Artificial Intelligence Research; Vol 3, No 2 (2019): December
Language eng
Rights Copyright (c) 2019 International Journal of Artificial Intelligence Research

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