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All Journal Rangkiang Mathematics Journal
Fajrin Putra Hanifi
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Mathematics Other

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Artificial Neural Network Model for Forecasting Inflation Rate in Indonesia Using Backpropagation Algorithm in Indonesia Fajrin Putra Hanifi; Syafriandi; Chairina Wirdiastuti; Nonong Amalita; Zilrahmi
Rangkiang Mathematics Journal Vol. 4 No. 1 (2025): Rangkiang Mathematics Journal
Publisher : Department of Mathematics, Universitas Negeri Padang (UNP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/rmj.v4i1.75

Abstract

Inflation is defined as a general and persistent rise in prices. Stable inflation is a prerequisite for sustainable Inflation, defined as a general and persistent rise in prices. Stable inflation is a prerequisite for sustainable economic growth. The importance of controlling inflation is based on the consideration that high and unstable inflation hurts the socio-economic conditions of the community. In this context, government and economic agents must know the future inflation rate. The backpropagation algorithm forecasting method can be a mathematical tool to forecast future inflation rates. The best forecasting model is obtained from applying the backpropagation algorithm, namely ANN BP (12,2,1), with a mean square error value of 0.15 and an absolute percentage error value of 11.09%. Based on these results, the back-propagation algorithm in artificial neural networks can accurately forecast the inflation rate. Thus, it is hoped that this research can be used in economic decision-making.
Co-Authors Chairina Wirdiastuti Nonong Amalita Syafriandi Zilrahmi, Zilrahmi