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Grisella Estefania R
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Humanities Education Social Sciences

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Rainfall Forecasting Using Seasonal Autoregressive Integrated Moving Average Method And Decomposition Method in Pangkalan Bun Grisella Estefania R; Agustini Tripena; Triyani, Triyani
PESHUM : Jurnal Pendidikan, Sosial dan Humaniora Vol. 4 No. 4: Juni 2025
Publisher : CV. Ulil Albab Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56799/peshum.v4i4.9431

Abstract

Flash floods and droughts that often occur in Pangkalan Bun, Central Kalimantan are clear evidence of the impact of climate change. Global climate change has caused an increase in the frequency and intensity of extreme weather, including significant fluctuations in rainfall. The purpose of this study is to create the best model for predicting rainfall using the SARIMA method and the Decomposition method in the case of data in Pangkalan Bun. The data used are rainfall data in Pangkalan Bun as many as 180 observations (January 2009 - December 2023). Rainfall data is divided into training data and testing data. Training data is used to determine the forecasting model using the SARIMA method and the Decomposition method. The results, the SARIMA (1,0,1) (1,0,1)12 model gives the lowest MAPE value compared to other decomposition models, namely a MAPE value of 26.05%. The MAPE value indicates that the SARIMA (1,0,1) (1,0,1)12 model is suitable for use in predicting future data. The forecast results show that the highest rainfall will occur in November 2024 at 263.97 mm and the lowest in August 2024 at 106.26 mm.
Co-Authors Agustini Tripena Triyani Triyani