Pemodelan SARIMA terhadap Curah Hujan Bulanan di Provinsi Jawa Barat
Keywords:
Rainfall, SARIMA;, Time Series, West Java
Abstract
Rainfall is a hydrometeorological phenomenon that has complex seasonal patterns and temporal fluctuations. This study aims to model monthly rainfall in West Java Province using the Seasonal Autoregressive Integrated Moving Average (SARIMA) approach based on 2015–2024 data. The research stages include data stationarity analysis, model order identification, parameter estimation, and residual diagnostic tests to ensure model validity. The best model selection is carried out by comparing the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) values. The analysis results show that the SARIMA model is able to represent seasonal patterns and monthly rainfall dynamics with a good level of accuracy. This model is expected to be a reliable mathematical basis for monthly rainfall forecasting in West Java Province.
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Cerqueira, V., Torgo, L., Mozetiˇ, I., & Tec, L. (2019). Evaluating time series forecasting models. 1–28.
Dickey, D. A., & Fuller, W. A. (2025). Distribution of the Estimators for With a Unit Root n n. 74(366), 427–431.
Ensafi, Y., Hassanzadeh, S., Zhang, G., & Shah, B. (2022). International Journal of Information Management Data Insights Time-series forecasting of seasonal items sales using machine learning – A comparative analysis. International Journal of Information Management Data Insights, 2(1), 100058. https://doi.org/10.1016/j.jjimei.2022.100058
Hasan, P., Khan, T. D., & Abedin, M. (2025). Temporal trends and forecasting of respiratory mortality in Bangladesh : A SARIMA model for seasonal mortality risk and public health action. https://doi.org/10.1177/22799036251395248
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Id, J. W., Agampodi, S., & Nr, A. (2022). SARIMA and ARDL models for predicting leptospirosis in Anuradhapura district Sri. 1–18. https://doi.org/10.1371/journal.pone.0275447
Junaedi, L., Damastuti, N., & Widodo, A. (2025). Penerapan Metode Seasonal ARIMA ( SARIMA ) untuk Peramalan Penjualan Barang dengan Pola Musiman Tahunan. 01, 38–48.
Khalil, A., Ullah, S., Khan, S. A., Manzoor, S., Gul, A., Shafiq, M., Khalil, A., Ullah, S., Khan, S. A., Manzoor, S., Gul, A., Khalil, A., Ullah, S., Khan, S. A., & Manzoor, S. (2017). Applying Time Series and a Non-Parametric Approach to Predict Pattern , Variability , and Number of Rainy Days Per Month Applying Time Series and a Non-Parametric Approach to Predict Pattern , Variability , and Number of Rainy Days Per Month. 26(2), 635–642. https://doi.org/10.15244/pjoes/65155
Khoshvaght, H., Ramyad, R., Razmjou, A., & Khiadani, M. (2025). Journal of Environmental Chemical Engineering A critical review on selecting performance evaluation metrics for supervised machine learning models in wastewater quality prediction. Journal of Environmental Chemical Engineering, 13(6), 119675. https://doi.org/10.1016/j.jece.2025.119675
Noor, T. H., Almars, A. M., Alwateer, M., Almaliki, M., & Gad, I. (2022). SARIMA : A Seasonal Autoregressive Integrated Moving Average Model for Crime Analysis in Saudi Arabia. 1–14.
Nurhasanah, D., Maulidya, A., & Dwi, M. (2022). Forecasting International Tourist Arrivals in Indonesia Using SARIMA Model. 2(1), 19–25.
Ruhiat, D., Masrulloh, E. S., & Azis, F. (2022). Forecasting Data Time Series Berpola Musiman Menggunakan Model SARIMA ( Studi Kasus : Sungai Cipeles-Warungpeti ). 2, 39–50.
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Tian, C. W., Wang, H., & Luo, X. M. (2019). Time-series modelling and forecasting of hand , foot and mouth disease cases in China from 2008 to 2018. 2008–2010.
Usmani, M., Memon, Z. A., Zulfiqar, A., & Qureshi, R. (2024). Preptimize : Automation of Time Series Data Preprocessing and Forecasting.
Zhang, X., & Cao, W. (2025). Research on Time Series Forecasting Method Based on Autoregressive Integrated Moving Average Model with Zonotopic Kalman Filter.
