Penerapan Metode SARIMA untuk Peramalan Curah Hujan Dasarian di Stasiun Klimatologi Banten
DOI:
https://doi.org/10.29303/goescienceed.v7i4.3335Keywords:
Rainfall, Ten-Day Period (Dasarian), SARIMA, Seasonal Pattern, Forecasting.Abstract
Rainfall plays an important role in various aspects of life, particularly in water resource management and agriculture. This study aims to identify the temporal characteristics and seasonal patterns of dasarian (10-day) rainfall, to formulate and select an appropriate SARIMA model, and to evaluate its forecasting performance on out-of-sample data at the Banten Climatology Station. The data analyzed cover the period from Dasarian I of January 1976 to Dasarian I of January 2025, totaling 1,765 observations, divided into training and testing data. The analysis stages include data exploration, stationarity testing, model identification via ACF and PACF, estimation of several candidate models, model selection based on AIC and BIC, residual diagnostics, and forecast evaluation using RMSE and MAE. The results show that rainfall has an annual seasonal pattern with a period of 36 dasarians and is stationary in mean without either nonseasonal or seasonal differencing. The SARIMA(1,0,1)(1,0,1)36 model was selected because it produced the lowest AIC and BIC and had significant parameters. Residual diagnostics showed no autocorrelation. The residuals exhibited non-uniform variance (heteroskedasticity) across year groups; however, the ARCH effect test was not significant, so there was no evidence of conditional heteroskedasticity influenced by past squared residuals. The residuals were not normally distributed because of extreme values. Evaluation on the testing data produced an RMSE of 59.61 mm and an MAE of 44.94 mm. The model can represent the seasonal pattern but produced smoother predictions, so it did not fully capture extreme rainfall events. The selected model is better suited for forecasting the average seasonal pattern of dasarian rainfall than for capturing the magnitude of extreme events.
References
Amelia, R., Kustiawan, E., Sulistiana, I., & Dalimunthe, D. Y. (2022). Forecasting rainfall in pangkalpinang city using seasonal autoregressive integrated moving average with exogenous (Sarimax). Barekeng, 16(1), 137–146. https://doi.org/10.30598/barekengvol16iss1pp137-146
Anas, M. A., Aswad, H., & Sar, M. ud. (2025). Model intensitas hujan akibat pemanasan global pada DAS Cidanau Kabupaten Serang Provinsi Banten. Jurnal Teknik Industri Terintegrasi, 8(1), 1401–1413. https://doi.org/10.31004/jutin.v8i1.42063
Ariska, M., Suhadi, Supari, Irfan, M., & Iskandar, I. (2024). Spatio-Temporal Variations of Indonesian Rainfall and Their Links to Indo-Pacific Modes. Atmosphere, 15(9). https://doi.org/10.3390/atmos15091036
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2016). TIME SERIES ANALYSIS (5th ed.).
Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? -Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247–1250. https://doi.org/10.5194/gmd-7-1247-2014
Damor, P. A., Ram, B., & Kunapara, A. N. (2023). Stochastic Time Series Analysis, Modeling, and Forecasting of Weekly Rainfall Using Sarima Model. International Journal of Environment and Climate Change, 13(12), 773–782. https://doi.org/10.9734/ijecc/2023/v13i123740
Defiyanti, S., Nurina Sari, B., & Nur Padilah, T. (2024). Optimasi Pertanian Padi: Peramalan Curah Hujan Berbasis Arima Untuk Penentuan Waktu Tanam Yang Tepat. Jurnal Teknologi Informasi Dan Ilmu Komputer, 11(6), 1377–1384. https://doi.org/10.25126/jtiik.2024118682
Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity With Estimates of The Variance of United Kingdom Inflation. In Source: Econometrica (Vol. 50, Number 4).
Jarque, C. M., & Bera, A. K. (1987). A Test for Normality of Observations and Regression Residuals Author(s): Carlos M. Jarque and Anil K. Bera Reviewed work(s): Source: International Statistical Review / Revue Internationale de Statistique Normality o f Observations and Regression Residuals. In International Statistical Review (Vol. 55, Number 2).
Lestari, S., King, A., Vincent, C., Karoly, D., & Protat, A. (2019). Seasonal dependence of rainfall extremes in and around Jakarta, Indonesia. Weather and Climate Extremes, 24. https://doi.org/10.1016/j.wace.2019.100202
Ljung, G. M., & Box, G. E. P. (1978). On a measure of lack of fit in time series models. In Biometrika (Vol. 68, Number 2). http://biomet.oxfordjournals.org/
Mondiana, Y. Q., Zairina, A., & Sari, R. K. (2022). Prediksi Peluang Kejadian Curah Hujan Ekstrim Dan Implikasi Pengelolaan Sumberdaya Air. Journal of Forest Science Avicennia, 4(2), 96–101. https://doi.org/10.22219/avicennia.v4i2.19695
Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to time series analysis and forecasting (2nd ed.). John Wiley & Sons.
Mulsandi, A., Koesmaryono, Y., Hidayat, R., Faqih, A., Sopaheluwakan, A., Studi Meteorologi, P., & Tinggi Meteorologi Klimatologi dan Geofisika, S. (2024). On The Interannual Variability of Indonesia Monsoon on The Interannual Variability of Indonesian Monsoon Rainfall (IMR): A Literature Review of The Role of Its External Forcing. https://psl.noaa.gov/
Rahmat, J., Budiawati, Y., Universitas Sultan Ageng Tirtayasa Jl Raya Palka km, S., Pabuaran, K., & Serang, K. (2025). Dampak Perubahan Iklim dan Paparan Bencana terhadap Ketahanan Pangan di Provinsi Banten, Indonesia (2018-2023). Mimbar Agribisnis: Jurnal Pemikiran Masyarakat Ilmiah Berwawasan Agribisnis, 11(2), 2935–2946.
Supari, Tangang, F., Salimun, E., Aldrian, E., Sopaheluwakan, A., & Juneng, L. (2018). ENSO modulation of seasonal rainfall and extremes in Indonesia. Climate Dynamics, 51(7–8), 2559–2580. https://doi.org/10.1007/s00382-017-4028-8
