Artificial Neural Network Model for Forecasting Inflation Rate in Indonesia Using Backpropagation Algorithm in Indonesia

Authors

  • Fajrin Putra Hanifi Universitas Negeri Padang
  • Syafriandi Universitas Negeri Padang
  • Chairina Wirdiastuti Universitas Negeri Padang
  • Nonong Amalita Universitas Negeri Padang
  • Zilrahmi Universitas Negeri Padang

DOI:

https://doi.org/10.24036/rmj.v4i1.75

Keywords:

Backpropagation Algorithm, Inflation Rate, Artificial Neural Network, Forecasting, Model

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.

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Published

2025-04-27

How to Cite

Fajrin Putra Hanifi, Syafriandi, Chairina Wirdiastuti, Nonong Amalita, & Zilrahmi. (2025). Artificial Neural Network Model for Forecasting Inflation Rate in Indonesia Using Backpropagation Algorithm in Indonesia. Rangkiang Mathematics Journal, 4(1), 24–31. https://doi.org/10.24036/rmj.v4i1.75