Forecasting Indonesia's Gross Domestic Product Using Extreme Learning Machine and Double Exponential Smoothing
DOI:
https://doi.org/10.55927/modern.v4i1.13265Keywords:
Gross Domestic Product, Extreme Learning Machine (ELM), Double Exponential Smoothing, ForecastingAbstract
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.
Downloads
References
Alfiyatin, A. N., Mahmudy, W. F., Ananda, C.F., & Anggodo, Y. P (2019). Penerapan extreme learning machine ( ELM) untuk peramalan laju inflasi di Indonesia Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK).
Ashar, N. M., Cholissodin, I., & Dewi, C, 2018, Penerapan Metode Extreme Learning Machine (ELM) Untuk Memprediksi Jumlah Produksi Pipa Yang Layak (Studi Kasus Pada PT.
Dardiri, A., 2018, Indeks Harga Konsumen 8 Kota di Provinsi Jawa Timur, BPS Provinsi Jawa Timur.
Fadilla, A., & Purnamasari, A. (2021). Pengaruh Inflasi Terhadap Pertumbuhan Ekonomi Indonesia. Jurnal Pemikiran dan Pengembangan Ekonomi Syariah, 17 - 27.
Fausett, L. V, 1994, Fundamentals of Neural Networks Architectures, Algoritms, and Application, Prentce Hall, London Huang, G.B., Zhu, Q.Y. and Siew, C.K, 2006 Extreme Learning Machine; Theory andApplications, Neuroncomputing, 70, 489- 501, http://dx.doi.org?10.16/j.neucom. 2005,12, 126
Fausett, L. V, 1994, Fundamentals of Neural Networks Architectures, Algorithms, and Applications, Prentice Hall, London
Hidayat A.A., 2010, Metode Penelitian Kesehatan Paradigma Kuantitatif, Jakarta, Health Books.
Huang G.-B., Zhu, Q.-Y & siew, C.-K (2006) Extreme Learning Machine: Theori and aplications. Neurocompoting 70, 489-501
KHI Pipe Industries), Volume 2, No 11 Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, Malang.
Kurniasih, I. H., Furqon, M. T., & Adinugroho, S.(2020) Prediksi Pertumbuhan Penduduk di Kota
Kusmadewi, S, 2003, Artificial Intellingence (Teknik Dan Aplikasinya), Graha Ilmu, Yogyakarta
Ludin, J. (2020). Analisis Deret Berkala.In J. Ludin, Analisis Deret Berkala (p.1). Pusat Pendidikan dan Pelatihan Badan Pusat Statistik.
Malang menggunakan metode Extreme Learning Machine (ELM) Teknologi Informasi dan Ilmu Komputer, 4(2) , 509-516 Jurnal Pengembangan
Markidakis, Wheelwright, & Mcgee. (1999). Metode dan Aplikasi Peramalan. In Markidakis, Wheelwright,
Mcgee, & D. Pang (Ed.), Metode dan Aplikasi Peramalan (I. H. Suminto, Trans., Vol II, p. 57). Jakarta, Jakarta, Indonesia: Binaruma Aksara.
Markidakis. (1999). Metode dan Aplikasi Peramalan. In Makridakis Metode dan Aplikasi Peramalan (P. 101). Jakarta: Binarupa Aksara
Martua, M.N, (2018) peramalan Produk Domestik Bruto sector pertanian 2019. Medan Sumatera Utara Indonesia: universitas sumatera utara.
Montgomery, D, C., Jennings, C. L., & Kulahci, M. (2008). Introduktion to Times Series Analysis and Forecasting. (D. J. Balding, & dkk, Eds.) Canada: Wiley Interscience.
Nasution, Damhuri, dan Hendranata, Anton. 2014. Laporan Akhir: Estimasi PDB Gap diIndonesia, Jakarta: Kementrian Keuangan RI
Setyawan Y, dkk, 2018, Statistik Dasar, AKPRIND PRESS, Yogyakarta.
Setyowati, W A. E dkk, 2014, Skrining Fitokirmia dan Identifikasi Komponen Utama Ekstrak Metanol Kulit Durian (Durio zibethinus Murr.) Varietas Petruk, (Halaman 271-280) Jurnal seminar Nasional Kimia dan Pendidikan Kimia VI, Surakarta.
Toron, L. L, (2021), Perbandingan Extreme Learning Machine (Elm) dan Double Exponential Smoothing untuk meramalkan Produk Domestik Regional Bruto di Provinsi Ntt, Skripsi, Institut Sains & Teknologi Akprind, Yogyakarta
Utari, G.A Diah, Cristina S, Retni, Pambudi, Sudiro. 2016. Inflasi di Indonesia: Karakteristik dan Penggendaliannya. Jakarta: Bank Indonesia institute
Wardhani, S.P., 2021. Pengaplikasian Extreme Learning Machine Untuk Peramalan data Time Series. Skripsi. Yogyakarta: Universitas Gadja Madah.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Anggelina Yuniance Bria, Miswanto , Frasto Biyanto, Baldric Siregar

This work is licensed under a Creative Commons Attribution 4.0 International License.