Artificial Neural Network Model for Forecasting Inflation Rate in Indonesia Using Backpropagation Algorithm in Indonesia
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| Title: | Artificial Neural Network Model for Forecasting Inflation Rate in Indonesia Using Backpropagation Algorithm in Indonesia |
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| Authors: | null Fajrin Putra Hanifi, null Syafriandi, null Chairina Wirdiastuti |
| Source: | Rangkiang Mathematics Journal. 4:24-31 |
| Publisher Information: | Universitas Negeri Padang, 2025. |
| Publication Year: | 2025 |
| Description: | 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. |
| Document Type: | Article |
| ISSN: | 2716-0734 2716-0726 |
| DOI: | 10.24036/rmj.v4i1.75 |
| Accession Number: | edsair.doi...........bd90dc2a38f296e067ae2c74b50484da |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | 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. |
|---|---|
| ISSN: | 27160734 27160726 |
| DOI: | 10.24036/rmj.v4i1.75 |