Penerapan Metode SARIMA untuk Peramalan Frekuensi Curah Hujan Bulanan di Sumatera Utara
Abstract
Variabilitas iklim ekstrem di Provinsi Sumatera Utara menuntut strategi mitigasi bencana yang presisi, mengingat tingginya risiko bahaya hidrometeorologi di wilayah tersebut. Penelitian ini bertujuan untuk membangun model peramalan curah hujan yang tangguh (robust) dengan mengintegrasikan data deret waktu (2010–2025) dari Copernicus Data Space Ecosystem menggunakan pendekatan Seasonal Autoregressive Integrated Moving Average (SARIMA). Melalui prosedur iteratif Box-Jenkins dan evaluasi kriteria informasi (AIC), model $SARIMA(0,1,1)(0,1,1)_{12}$ teridentifikasi sebagai estimator terbaik yang mampu menangkap pola musiman dan tren stokastik secara akurat, dengan validasi Root Mean Square Error (RMSE) sebesar 82,11 mm. Proyeksi untuk tahun 2026 mengindikasikan eskalasi curah hujan yang signifikan selama periode Oktober hingga Desember, yang menandai fase kritis bagi potensi banjir. Temuan ini memberikan dasar ilmiah kuantitatif bagi pembuat kebijakan untuk memperkuat sistem peringatan dini dan mengoptimalkan manajemen risiko bencana nasional.
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Anuradha, G., Muppidi, S., Karnati, R., & Rao, K. P. (2025). Rainfall prediction using time series data based on RSJSO_BiLSTM.
International Journal of Machine Learning and Cybernetics, 16(5–6), 3907–3926. https://doi.org/10.1007/s13042-024-02488-7
Ariska, M., Suhadi, Supari, Irfan, M., & Iskandar, I. (2024a). Detection of Dominant Rainfall Patterns in Indonesian Regions Using Empirical Orthogonal Function (EOF) and Its Relation with ENSO and IOD Events. Science and Technology Indonesia, 9(4), 1009–1023. https://doi.org/10.26554/sti.2024.9.4.1009-1023
Ariska, M., Suhadi, Supari, Irfan, M., & Iskandar, I. (2024b). Spatio-Temporal Variations of Indonesian Rainfall and Their Links to Indo-Pacific Modes. Atmosphere, 15(9), 1036. https://doi.org/10.3390/atmos15091036
Azad, A. S., Sokkalingam, R., Daud, H., Adhikary, S. K., Khurshid, H.,
Mazlan, S. N. A., & Rabbani, M. B. A. (2022). Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study. Sustainability, 14(3), 1843. https://doi.org/10.3390/su14031843
Copernicus Data Space Ecosystem. (n.d.).
Fowler, H. J., Lenderink, G., Prein, A. F., Westra, S., Allan, R. P., Ban, N., Barbero, R., Berg, P., Blenkinsop, S., Do, H. X., Guerreiro, S., Haerter, J. O., Kendon, E. J., Lewis, E., Schaer, C., Sharma, A., Villarini, G., Wasko, C., & Zhang, X. (2021). Anthropogenic intensification of short-duration rainfall extremes. Nature Reviews Earth & Environment, 2(2), 107–122. https://doi.org/10.1038/s43017-020-00128-6
Hermawan, E., Lubis, S. W., Harjana, T., Purwaningsih, A., Risyanto, R., Ridho, A., Andarini, D. F., Ratri, D. N., & Widyaningsih, R. (2022a). Large-Scale Meteorological Drivers of the Extreme Precipitation Event and Devastating Floods of Early-February 2021 in Semarang, Central Java, Indonesia. Atmosphere, 13(7), 1092. https://doi.org/10.3390/atmos13071092
Hermawan, E., Lubis, S. W., Harjana, T., Purwaningsih, A., Risyanto, R., Ridho, A., Andarini, D. F., Ratri, D. N., & Widyaningsih, R. (2022b). Large-Scale Meteorological Drivers of the Extreme Precipitation Event and Devastating Floods of Early-February 2021 in Semarang, Central Java, Indonesia. Atmosphere, 13(7), 1092. https://doi.org/10.3390/atmos13071092
Hermawan, E., Risyanto, R., Purwaningsih, A., Ratri, D. N., Ridho, A., Harjana, T., Andarini, D. F., Satyawardhana, H., & Sujalu, A. P. (2025). Characteristics of Mesoscale Convective Systems and Their Impact on Heavy Rainfall in Indonesia’s New Capital City, Nusantara, in March 2022. Advances in Atmospheric Sciences, 42(2), 342–356. https://doi.org/10.1007/s00376-024-4102-1
Kumar, V., Kedam, N., Sharma, K. V., Khedher, K. M., & Alluqmani, A. E. (2023). A Comparison of Machine Learning Models for Predicting Rainfall in Urban Metropolitan Cities. Sustainability, 15(18), 13724. https://doi.org/10.3390/su151813724
Kurniadi, A., Weller, E., Salmond, J., & Aldrian, E. (2024). Future projections of extreme rainfall events in Indonesia. International Journal of Climatology, 44(1), 160–182. https://doi.org/10.1002/joc.8321
Marzuki, M., Ramadhan, R., Yusnaini, H., Vonnisa, M., Safitri, R., & Yanfatriani, E. (2023). Changes in Extreme Rainfall in New Capital of Indonesia (IKN) Based on 20 Years of GPM-IMERG Data. Trends in Sciences, 20(11), 6935. https://doi.org/10.48048/tis.2023.6935
Nwokike, C. C., Offorha, B. C., Obubu, M., Ugoala, C. B., & Ukomah, H. I. (2020). Comparing SANN and SARIMA for forecasting frequency of monthly rainfall in Umuahia. Scientific African, 10, e00621. https://doi.org/10.1016/j.sciaf.2020.e00621
Pamadya, O., Juwono, P. T., Andawayanti, U., & Asmaranto, R. (2022). Detection of Climate Change in the Kedungsoko Irrigation Area – Nganjuk, Indonesia. Journal of Hunan University Natural Sciences, 49(4), 271–280. https://doi.org/10.55463/issn.1674-2974.49.4.27
Purwaningsih, A., Lubis, S. W., Hermawan, E., Andarini, D. F., Harjana, T., Ratri, D. N., Ridho, A., Risyanto, & Sujalu, A. P. (2022a). Moisture Origin and Transport for Extreme Precipitation over Indonesia’s New Capital City, Nusantara in August 2021. Atmosphere, 13(9), 1391. https://doi.org/10.3390/atmos13091391
Purwaningsih, A., Lubis, S. W., Hermawan, E., Andarini, D. F., Harjana, T., Ratri, D. N., Ridho, A., Risyanto, & Sujalu, A. P. (2022b). Moisture Origin and Transport for Extreme Precipitation over Indonesia’s New Capital City, Nusantara in August 2021. Atmosphere, 13(9), 1391. https://doi.org/10.3390/atmos13091391
Putra, M., Rosid, M. S., & Handoko, D. (2024). High-Resolution Rainfall Estimation Using Ensemble Learning Techniques and Multisensor Data Integration. Sensors, 24(15), 5030. https://doi.org/10.3390/s24155030
Ramli, I., Rusdiana, S., Achmad, A., Azizah, & Yolanda, M. E. (2023). Forecasting of Rainfall Using Seasonal Autoregreressive Integrated Moving Average (SARIMA) Aceh, Indonesia. Mathematical Modelling of Engineering Problems, 10(2), 501–508. https://doi.org/10.18280/mmep.100216
Sham, F. A. F., El-Shafie, A., Jaafar, W. Z. B. W., Adarsh, S., Sherif, M., & Ahmed, A. N. (2025). Improving rainfall forecasting using deep learning data fusing model approach for observed and climate change data. Scientific Reports, 15(1), 27872. https://doi.org/10.1038/s41598-025-13567-2
Simanjuntak, F., Jamaluddin, I., Lin, T.-H., Siahaan, H. A. W., & Chen, Y.-N. (2022a). Rainfall Forecast Using Machine Learning with High Spatiotemporal Satellite Imagery Every 10 Minutes. Remote Sensing, 14(23), 5950. https://doi.org/10.3390/rs14235950
Simanjuntak, F., Jamaluddin, I., Lin, T.-H., Siahaan, H. A. W., & Chen, Y.-N. (2022b). Rainfall Forecast Using Machine Learning with High Spatiotemporal Satellite Imagery Every 10 Minutes. Remote Sensing, 14(23), 5950. https://doi.org/10.3390/rs14235950
Suhartono. (2011). Time Series Forecasting by using Seasonal Autoregressive Integrated Moving Average: Subset, Multiplicative or Additive Model. Journal of Mathematics and Statistics, 7(1), 20–27. https://doi.org/10.3844/jmssp.2011.20.27
Sulaiman, A., Osaki, M., Takahashi, H., Yamanaka, M. D., Susanto, R. D., Shimada, S., Kimura, K., Hirano, T., Wetadewi, R. I., Sisva, S., Kato, T., Kozan, O., Kubo, H., Awaluddin, A., & Tsuji, N. (2023). Peatland groundwater level in the Indonesian maritime continent as an alert for El Niño and moderate positive Indian Ocean dipole events. Scientific Reports, 13(1), 939. https://doi.org/10.1038/s41598-023-27393-x
Sung, W.-T., Devi, I. V., & Hsiao, S.-J. (2021). Early Warning of Impending Flash Flood Based on AIoT. https://doi.org/10.21203/rs.3.rs-850371/v1
Uphade, D. B., Muley, A. A., & Phad, G. S. (2025). Evaluation of Seasonal Temperature Variability in Maharashtra’s Metropolitan Cities using Predictive Modelling Perspective. INTERNATIONAL JOURNAL OF AGRICULTURAL AND STATISTICAL SCIENCES, 21(01), 183. https://doi.org/10.59467/IJASS.2025.21.183
Yamamoto, K., Sayama, T., & Apip. (2021). Impact of climate change on flood inundation in a tropical river basin in Indonesia. Progress in Earth and Planetary Science, 8(1), 5. https://doi.org/10.1186/s40645-020-00386-4
Yanfatriani, E., Marzuki, M., Vonnisa, M., Razi, P., Hapsoro, C. A.,
Ramadhan, R., & Yusnaini, H. (2024a). Extreme Rainfall Trends and Hydrometeorological Disasters in Tropical Regions: Implications for Climate Resilience. Emerging Science Journal, 8(5), 1860–1874. https://doi.org/10.28991/ESJ-2024-08-05-012
Yanfatriani, E., Marzuki, M., Vonnisa, M., Razi, P., Hapsoro, C. A., Ramadhan, R., & Yusnaini, H. (2024b). Extreme Rainfall Trends and Hydrometeorological Disasters in Tropical Regions: Implications for Climate Resilience. Emerging Science Journal, 8(5), 1860–1874. https://doi.org/10.28991/ESJ-2024-08-05-012
Yavuz, V. S. (2025). Forecasting monthly rainfall and temperature patterns in Van Province, Türkiye, using ARIMA and SARIMA models: a long-term climate analysis. Journal of Water and Climate Change, 16(2), 800–818. https://doi.org/10.2166/wcc.2025.798
Zaini, A. Z. A., Vonnisa, M., & Marzuki, M. (2024). Impact of different ENSO positions and Indian Ocean Dipole events on Indonesian rainfall. Vietnam Journal of Earth Sciences. https://doi.org/10.15625/2615-9783/19926
DOI: https://doi.org/10.26877/imajiner.v8i2.26717
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