Forecasting Indonesia's Gross Domestic Product Using Extreme Learning Machine and Double Exponential Smoothing

Authors

  • Anggelina Yuniance Bria Program Studi Magister Manajemen, Sekolah Tinggi Ilmu Ekonomi YKPN Yogyakarta
  • Miswanto Program Studi Magister Manajemen, Sekolah Tinggi Ilmu Ekonomi YKPN Yogyakarta
  • Frasto Biyanto Program Studi Magister Manajemen, Sekolah Tinggi Ilmu Ekonomi YKPN Yogyakarta
  • Baldric Siregar Program Studi Magister Manajemen, Sekolah Tinggi Ilmu Ekonomi YKPN Yogyakarta

DOI:

https://doi.org/10.55927/modern.v4i1.13265

Keywords:

Gross Domestic Product, Extreme Learning Machine (ELM), Double Exponential Smoothing, Forecasting

Abstract

Gross Domestic Product (GDP) is one of the main indicators used to measure the economic condition of a country. Stable economic growth is crucial for achieving societal well-being; however, there are various factors that influence GDP fluctuations. Internal factors, such as ineffective fiscal and monetary policies, as well as external factors like changes in global economic conditions and geopolitical instability, can lead to instability in GDP growth. To improve GDP stability, appropriate fiscal and monetary policies, infrastructure investments, productivity enhancement, and the promotion of industry and the creative economy are necessary. This study employs two forecasting methods, namely Extreme Learning Machine (ELM) and Double Exponential Smoothing (DES), to analyze and predict economic growth based on historical GDP data. The results show that both methods can be used effectively to predict GDP growth, with ELM demonstrating superior ability in producing more accurate forecasts. By applying the right methods, it is expected that stable GDP growth can be achieved, leading to improved societal well-being and advancing the nation's progress.

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Published

2025-01-09

How to Cite

Bria, A. Y., Miswanto, Biyanto, F. ., & Siregar, B. . (2025). Forecasting Indonesia’s Gross Domestic Product Using Extreme Learning Machine and Double Exponential Smoothing. Indonesian Journal of Contemporary Multidisciplinary Research, 4(1), 11–18. https://doi.org/10.55927/modern.v4i1.13265