Wind Speed Forecast Using the Triple Exponential Smoothing Method in Pangandaran Beach
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Jan 20, 2025
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Abstract
Indonesia as a maritime country with a long coastline, holds significant potential in the marine and tourism sectors. However, these sectors are often disrupted by adverse weather conditions, particularly irregular wind speeds. Accurate wind speed forecasting is therefore essential for disaster mitigation. This study aims to forecast wind speed at Pantai Pangandaran using the Triple Exponential Smoothing (TES) method, which is more effective in handling data fluctuations with trend and seasonal patterns. The data used includes daily data from January 2014 to September 2024. The results show that the TES method provides highly accurate forecasts, with a low error rate evaluated through an RMSE of 0.51 and a MAPE of 17.85% for wind speed. These forecasts are expected to support disaster mitigation, enhance safety, and improve the efficiency of activities in coastal areas, particularly at Pantai Pangandaran, in facing adverse weather conditions.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Hakim, D. K., Yuana Wangsa Putri Setiawan, Supriyono, & Maulida Ayu Fitriani4. (2025). Wind Speed Forecast Using the Triple Exponential Smoothing Method in Pangandaran Beach. Jurnal E-Komtek (Elektro-Komputer-Teknik), 8(2), 583-594. https://doi.org/10.37339/e-komtek.v8i2.2243
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[4] M. Ryan and S. Alfiandy, “Prediksi kecepatan angin 12 jam kedepan menggunakan Automatic Weather Observing System (AWOS) berbasis Regresi Linear,” Bul. GAW Bariri, vol. 3, no. 2, pp. 1–7, 2022, doi: 10.31172/bgb.v3i2.63.
[5] D. M. Khairina, Y. Daniel, and P. P. Widagdo, “Comparison of double exponential smoothing and triple exponential smoothing methods in predicting income of local water company,” J. Phys. Conf. Ser., vol. 1943, no. 1, 2021, doi: 10.1088/1742-6596/1943/1/012102.
[6] J. Vimala and A. Nugroho, “Forecasting penjualan obat menggunakan metode Single, Double, dan Triple Exponential Smoothing (sstudi kasus: Apotek Mandiri Medika),” IT-Explore J. Penerapan Teknol. Inf. dan Komun., vol. 1, no. 2, pp. 90–99, 2022, doi: 10.24246/itexplore.v1i2.2022.pp90-99.
[7] D. K. Hakim, R. Gernowo, and A. W. Nirwansyah, “Rainfall, wind speed, and temperature forecast using Triple Exponential Smoothing and Gradient Descent,” KnE Soc. Sci., vol. 2024, pp. 578–590, 2024, doi: 10.18502/kss.v9i6.15311.
[8] M. Yesilbudak, S. Sagiroglu, and I. Colak, “A new approach to very short term wind speed prediction using k-nearest neighbor classification,” Energy Convers. Manag., vol. 69, pp. 77–86, 2013, doi: 10.1016/j.enconman.2013.01.033.
[9] H. Susanto and D. K. Hakim, “Forecasting air temperature using the Triple Exponential Smoothing Method,” vol. 8, no. 1, pp. 1–9, 2024.
[10] N. Maaroufi, M. Najib, and M. Bakhouya, “Predicting the future is like completing a painting : towards a novel method for Time-Series Forecasting,” IEEE Access, vol. 9, pp. 119918–119938, 2021, doi: 10.1109/ACCESS.2021.3101718.
[11] A. E. Raftery, Time series analysis with applications in R, vol. 20, no. 2. 1985. doi: 10.1016/0377-2217(85)90052-9.
[12] I. D. Sumitra, I. Basri, A. Pamungkas, and R. Puspasari, “Covid-19 forecast using Holt-Winters exponential smoothing Covid-19 forecast using Holt-Winters exponential smoothing”, doi: 10.1088/1742-6596/1882/1/012033.
[13] Y. A. Jatmiko, R. L. Rahayu, and G. Darmawan, “Perbandingan keakuratan hasil peramalan produksi bawang merah metode Holt-Winters dengan Singular Spectrum Analysis (SSA),” vol. 03, no. 01, pp. 13–22, 2017.
[14] R. B. Saputro, K. P. Kartika, and W. D. Puspitasari, “Implementation of the Triple Exponential Smoothing Method for predicting helmet sales,” JOINCS (Journal Informatics, Network, Comput. Sci., vol. 5, no. 2, pp. 30–34, 2022, doi: 10.21070/joincs.v5i2.1607.
[15] Masniar, A. R. Histiarini, and I. F. Said, “Analisis dampak pandemi Covid-19 terhadap jumlah permintaan produk teh 2m (Studi Kasus: Cv. Harum Manis Sorong),” vol. 5, no. 1, pp. 8–14, 2020.
[16] E. Vivas and H. Allende-cid, “A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score,” 2020, doi: 10.3390/e22121412.
[17] F. Asfahanif and S. Amri, “Analisis Peristiwa Angin Kencang dengan Citra Satelit Himawari-8 (Studi Kasus: Bangkalan, 17 Oktober 2021),” J. Geogr. Media Inf. Pengemb. dan Profesi Kegeografian, vol. 19, no. 2, pp. 83–91, 2022, doi: 10.15294/jg.v19i2.34826.