Article Sidebar

Submitted
Mar 1, 2026
Accepted
Jun 30, 2026
Published
Jun 30, 2026
Download
PDF
Statistic
Read Counter : 14 Download : 6

Main Article Content

Abstract

Batang Regency has experienced continuous economic growth in recent years, making information on changes in Gross Regional Domestic Product (GRDP) essential for supporting regional development planning. Reliable forecasting methods are needed to provide an overview of future economic conditions based on observed trends. Previous studies have applied either Holt’s Double Exponential Smoothing (DES) or Autoregressive Integrated Moving Average (ARIMA) models for economic forecasting; however, comparative evidence regarding the performance of these methods on Batang Regency’s GRDP data remains limited. Therefore, this study aims to compare the forecasting performance of both methods and generate GRDP forecasts for the period 2025–2029. This study utilized annual GRDP data at constant prices from 2010 to 2024, which showed an increasing trend and nonstationary characteristics. The analysis procedure included descriptive analysis, stationarity testing using the Augmented Dickey–Fuller (ADF) test, model estimation, and forecast accuracy evaluation using Mean Absolute Percentage Error (MAPE). The ADF test indicated that the series became stationary after first-order differencing, and several ARIMA models were evaluated using the Akaike Information Criterion (AIC), resulting in ARIMA(1,1,1) being selected as the best-performing model. However, in terms of forecasting accuracy, Holt’s DES outperformed ARIMA by producing a lower MAPE value of 2.15%, compared to 8.87% for ARIMA(1,1,1). Forecasts generated by both methods indicate that Batang Regency’s GRDP is expected to continue increasing during the period 2025–2029. These findings demonstrate that Holt’s DES provides more accurate forecasts for the GRDP data used in this study and contribute empirical evidence regarding the comparative performance of classical time series forecasting methods for regional economic planning. The results may serve as a reference for policymakers in formulating more targeted and sustainable development strategies.

Keywords

ARIMA Batang Regency Holt's DES forecasting Gross Regional Domestic Product

Article Details

Creative Commons License

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

How to Cite
Rahmaniyah, A., & Setyowisnu, G. E. (2026). EVALUATING THE ACCURACY OF HOLT’S DOUBLE EXPONENTIAL SMOOTHING AND ARIMA IN FORECASTING BATANG REGENCY’S GROSS REGIONAL DOMESTIC PRODUCT (2025–2029). Parameter: Journal of Statistics, 6(1), 32-42. https://doi.org/10.22487/27765660.2026.v6.i1.18028

References

  1. Abdy, M., Irwan, & Lukman, F. (2023). Penggunaan Metode Double Exponential Smoothing dalam Meramalkan Indeks Harga Konsumen (IHK) di Kota Makassar. Journal of Mathematics: Theory and Applications, 5(2), 61–66. https://doi.org/10.31605/jomta.v5i2.2874
  2. Artiyati, N., & Utari, D. T. (2024). Implementasi Metode Double Exponential Smoothing Dalam Peramalan Nilai PDRB Kota Tangerang Selatan Tahun 2023-2027. Emerging Statistics and Data Science Journal, 2(1), 77–84.
  3. Badan Pusat Statistik. (2025). Produk Domestik Regional Bruto Provinsi-Provinsi di Indonesia Menurut Lapangan Usaha (2020-2024).
  4. BPS Kabupaten Batang. (2024). Lembar Fakta PDRB Lapangan Usaha Kabupaten Batang 2024.
  5. Dalimunthe, D. Y. (2017). Analisis Peramalan Data Produk Domestik Regional Bruto (PDRB) Sebagai Tolak Ukur Kinerja Perekonomian Provinsi Kepulauan Bangka Belitung. Journal of Business and Economics (IJBE), 1(1), 19–27.
  6. Hayati, L. A., & Agustina, N. (2024). Aplikasi Model ARIMA dalam Peramalan Produk Domestik Regional Bruto (PDRB) Pertambangan dan Penggalian Kalimantan Selatan (Application of ARIMA Model in Forecasting Gross Regional Domestic Product (GRDP) Mining and Quarrying in South Kalimantan).
  7. Hyndman, R. J., & Athanasopoulos, George. (2018). Forecasting: principles and practice (3rd ed.). OTexts.
  8. Jansen, W. J., Jin, X., & de Winter, J. M. (2016). Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts. International Journal of Forecasting, 32(2), 411–436. https://doi.org/10.1016/j.ijforecast.2015.05.008
  9. Jijo, J. J. (2025). Modelling and Forecasting the GDP of G7 Countries Using Arima Model. Asian Journal of Economics, Business and Accounting, 25(3), 295–315. https://doi.org/10.9734/ajeba/2025/v25i31711
  10. Kementerian Koordinator Bidang Perekonomian Republik Indonesia. (2024). Mengusung Konsep The Smart & Sustainable Industrial Estate, Kawasan Industri Terpadu Batang Menjadi Garda Terdepan Investasi Sektor Industri. www.ekon.go.id
  11. Khoiri, H. A. (2023). Analisis Deret Waktu Univariat (Teori dan Pengolahan Data) (C. P. Hendrastati, Ed.; 1st ed.). UNIPMA Press.
  12. Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to Time Series Analysis and Forecasting (2nd ed.). Wiley.
  13. Muzakir, N. A., & Yahya, Muh. Z. (2025). Analisis Perbandingan Model Double Exponential Smoothing dan ARIMA untuk Prediksi Harga Beras di Indonesia. VARIANSI: Journal of Statistics and Its Application on Teaching and Research, 7(1), 7–20. https://doi.org/10.35580/variansiunm349
  14. Ratag, O. S. I., Rotinsulu, T. O., & Maramis, M. Th. B. (2025). Analisis Potensi dan Daya Saing Perekonomian Kota Kotamobagu Periode 2019-2023. Jurnal Berkala Ilmiah Efisiensi, 25(2), 41–46.
  15. Syaharani, M. N. (2023). Peramalan PDRB Kabupaten Jombang pada Periode Mendatang Menggunakan Metode Double Exponential Smoothing. In Universitas Negeri Malang (Vol. 8).
  16. Tarigan, V. (2023). Penerapan Metode Double Exponential Smoothing Untuk Memperediksi Jumlah Penjualan Springbed di PT. Masindo Karya Prima. Jurnal Informasi Polinema.
  17. Wibowo, A. Z. A., Pratama, B. P., Aulia, M., & Kartiasih, F. (2025). Perbandingan Model ARIMA dan Holt’s Double Exponential Smoothing Untuk Peramalan Harga Emas Indonesia 2025. Journal of Mathematics: Theory and Applications, 7(2), 68–84. https://doi.org/10.31605/jomta.v7i2.4934