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Prediction of Macroeconomic Growth Using Backpropagation Algorithms: A Review

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Název: Prediction of Macroeconomic Growth Using Backpropagation Algorithms: A Review
Autoři: Nur Fitri Hidayanti, null Syaharuddin, Nurul Hidayati Indra Ningsih, Ahmad Hulaimi, Zaenafi Ariani, Dedy Iswanto
Zdroj: International Journal of Scientific Research and Management (IJSRM). 13:8255-8266
Informace o vydavateli: Valley International, 2025.
Rok vydání: 2025
Popis: This study aims to evaluate the effectiveness of the backpropagation algorithm in predicting macroeconomic growth through a Systematic Literature Review (SLR) approach. The review analyzes literature sourced from reputable indexes such as Scopus, DOAJ, and Google Scholar, focusing on publications from the last decade (2013–2024). It examines various studies on the application of the backpropagation algorithm, including parameter settings and model selection that influence prediction accuracy. The findings indicate that careful parameter configuration, such as the number of neurons, hidden layers, and learning rates, along with appropriate model selection, significantly enhance the performance of the backpropagation algorithm in macroeconomic prediction. This study highlights that combining optimal techniques and accurate parameter configurations substantially improves prediction accuracy and efficiency. It provides valuable insights and practical guidance for researchers and practitioners in designing more reliable and effective macroeconomic prediction models.
Druh dokumentu: Article
ISSN: 2321-3418
DOI: 10.18535/ijsrm/v13i01.em07
Rights: CC BY
Přístupové číslo: edsair.doi...........2edf3bcc2c7cb2c8ebd67bf37a748ffb
Databáze: OpenAIRE
Popis
Abstrakt:This study aims to evaluate the effectiveness of the backpropagation algorithm in predicting macroeconomic growth through a Systematic Literature Review (SLR) approach. The review analyzes literature sourced from reputable indexes such as Scopus, DOAJ, and Google Scholar, focusing on publications from the last decade (2013–2024). It examines various studies on the application of the backpropagation algorithm, including parameter settings and model selection that influence prediction accuracy. The findings indicate that careful parameter configuration, such as the number of neurons, hidden layers, and learning rates, along with appropriate model selection, significantly enhance the performance of the backpropagation algorithm in macroeconomic prediction. This study highlights that combining optimal techniques and accurate parameter configurations substantially improves prediction accuracy and efficiency. It provides valuable insights and practical guidance for researchers and practitioners in designing more reliable and effective macroeconomic prediction models.
ISSN:23213418
DOI:10.18535/ijsrm/v13i01.em07