Optimization of Double Exponential Smoothing Model for Daily Earth Temperature Forecasting in Dayeuhluhur, Cilacap
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Jun 30, 2025
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Abstract
Global warming has caused an increase in the Earth's surface temperature, which has a significant impact on the environment and human life. This study aims to predict the daily surface temperature in Dayeuhluhur District, Cilacap, for the next one year using the Double Exponential Smoothing (DES) method. The data used comes from the NASA POWER platform with a time span of 2015 to 2025, including three main variables: earth surface temperature (TS), solar radiation (ALLSKY_SFC_SW_DWN), and maximum 10-meter wind speed (WS10M_MAX). Preprocessing was done by removing February 29 in leap years and applying annual differencing (lag 365) to stabilize the seasonal pattern. Smoothing parameters α and β were optimized based on Mean Absolute Percentage Error (MAPE) values. Results show a moderate and consistent increasing trend in temperature, with the best accuracy in the temperature variable (MAPE 2.41%), followed by solar radiation (21.56%) and wind speed (30.18%). This method proves effective in forecasting temperature with clear seasonal patterns and contributes to supporting data-driven climate change mitigation policies.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Ridzna Asep Purwanto, Hakim, D. K., Supriyono, & Harjono. (2025). Optimization of Double Exponential Smoothing Model for Daily Earth Temperature Forecasting in Dayeuhluhur, Cilacap. Jurnal E-Komtek, 9(1), 61-73. https://doi.org/10.37339/e-komtek.v9i1.2396
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[2] A. Tefel-Escudero, “Conflicts and Cooperation: A Game Theory Analysis of the Israeli–Palestinian Conflict,” Nuevas Tendencias, vol. 24, no. 107, pp. 13–17, 2022, doi: 10.15581/022.42490.
[3] W. B. Adi, F. P. Hirsan, and J. S. Adiansyah, “ANALISIS POLA SPASIAL SUHU PERMUKAAN DI KOTA MATARAM TERKAIT FENOMENA URBAN HEAT ISLAND (UHI) BERDASARKAN FAKTOR EMISIVITAS LAHAN, KERAPATAN VEGETASI DAN JUMLAH KENDARAAN,” Jur. Perenc. Wil. Dan Kota, Fak. Tek. 2Dosen Fak. Tek. Progr. Stud. Perenc. Wil. Dan Kota,3Dosen Fak. Tek. Progr. Stud. Pertambangan, Univ. Muhammadiyah Mataram, pp. 126–138, 2020, doi: Doi.org/ 10.24036/geografi/vol10-iss2/2361.
[4] D. Prabowo, W. Sekolah, T. Ilmu, K. Medan, A. Peneliti, and S. Utara, “Identifikasi Suhu Permukaan Tanah Dengan Metode Konversi Digital Numbermenggunakan Teknik Penginderaan Jauh Dansistem Informasi Geografi,” Teknol. Inf. dan Komun., vol. 6, no. 2, pp. 59–69, 2017, [Online]. Available: file:///C:/Users/ASUS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Prabowo et al. - 2017 - Identifikasi Suhu Permukaan Tanah Dengan Metode Konversi Digital Numbermenggunakan Teknik Penginderaan Jauh Dans.pdf
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[6] M. Pleños, “Time Series Forecasting Using Holt-Winters Exponential Smoothing: Application to Abaca Fiber Data,” Zesz. Nauk. SGGW w Warszawie - Probl. Rol. Światowego, vol. 22, no. 2, pp. 17–29, 2022, doi: 10.22630/prs.2022.22.2.6.
[7] B. Billah, M. L. King, R. D. Snyder, and A. B. Koehler, “Exponential smoothing model selection for forecasting,” Int. J. Forecast., vol. 22, no. 2, pp. 239–247, 2006, doi: 10.1016/j.ijforecast.2005.08.002.
[8] H. Y. Anggraeni, R. Aspriyani, and M. Ahmad, “Forecasting Daily Maximum and Minimum Air Temperatures in The Cilacap District Using Arima and Exponential Smoothing,” J. Mat. Sains dan Teknol., vol. 24, no. 2, pp. 48–61, 2023, doi: 10.33830/jmst.v24i2.5078.2023.
[9] X. Zhu et al., “A Hybrid Machine Learning Model Coupling Double Exponential Smoothing and ELM to Predict Multi-Factor Landslide Displacement,” Remote Sens., vol. 14, no. 14, 2022, doi: 10.3390/rs14143384.
[10] J. Lois, Y. Kurnia, D. Lasut, and I. Fenriana, “Aplikasi Pengolahan Data Mining Berbasis Web Menggunakan Algoritma Apriori Untuk Menganalisis Data Penjualan Toko Lumbini Mart,” J. Algor, vol. 3, no. 2, pp. 12–24, 2022, [Online]. Available: file:///D:/Donwload/Skripsi/Jurnal/rino,+Journal+editor,+Jerico+Lois+(12-22).pdf
[11] A. Sulaiman and A. Juarna, “Peramalan Tingkat Pengangguran Di Indonesia Menggunakan Metode Time Series Dengan Model Arima Dan Holt-Winters,” J. Ilm. Inform. Komput., vol. 26, no. 1, pp. 13–28, 2021, doi: 10.35760/ik.2021.v26i1.3512.
[12] N. POWER, “NASA Prediction Of Worldwide Energy Resources,” 2024. https://power.larc.nasa.gov/
[13] S. Hansun, “A New Approach of Brown’s Double Exponential Smoothing Method in Time Series Analysis,” Balk. J. Electr. Comput. Eng., vol. 4, no. 2, 2016, doi: 10.17694/bajece.14351.
[14] D. Febrian, S. I. Al Idrus, and D. A. J. Nainggolan, “The Comparison of Double Moving Average and Double Exponential Smoothing Methods in Forecasting the Number of Foreign Tourists Coming to North Sumatera,” J. Phys. Conf. Ser., vol. 1462, no. 1, 2020, doi: 10.1088/1742-6596/1462/1/012046.
[15] P. Pangestu and A. U. P. Santi, “Pengaruh Pendidikan Matematika Realistik Terhadap Suasana Pembelajaran Yang Menyenangkan Pada Pelajaran Matematika Sekolah Dasar,” FIBONACCI J. Pendidik. Mat. dan Mat., vol. 2, no. 2, p. 58, 2016, doi: 10.24853/fbc.2.2.58-71.
[16] T. Terttiaavini and T. S. Saputra, “Analisa Akurasi Penggunaan Metode Single Eksponential Smoothing untuk Perkiraan Penerimaan Mahasiswa Baru Pada Perguruan Tinggi XYZ,” J. Ilm. Inform. Glob., vol. 11, no. 1, pp. 64–68, 2020, doi: 10.36982/jig.v11i1.1075.
[17] A. Nurdina, D. Aryani, E. Venita, and S. Astiti, “Analisis Peramalan Permintaan Golang-Galing dalam Memaksimalkan Manajemen Rantai Pasok Menggunakan Metode Weighted Moving Average,” JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 4, p. 1167, 2022, doi: 10.30865/jurikom.v9i4.4551.
[18] C. V. Hudiyanti, F. A. Bachtiar, and B. D. Setiawan, “Perbandingan Double Moving Average dan Double Exponential Smoothing untuk Peramalan Jumlah Kedatangan Wisatawan Mancanegara di Bandara Ngurah Rai,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 3, pp. 2667–2672, 2019, [Online]. Available: file:///D:/Donwload/Skripsi/Jurnal/dina_oktavia,+%23%23default.groups.name.manager%23%23,+4791-99Z_Artikel-32740-1-2-20190109.pdf