Implementasi Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) untuk Memprediksi Curah Hujan di Kota Semarang

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Authors

DOI:

https://doi.org/10.32665/statkom.v3i2.3224

Keywords:

Rainfall, Forecasting, MAPE, SARIMA

Abstract

Background: Rainfall is one of the important factors that has a significant impact on various aspects of life, especially in urban areas such as Semarang. Significant fluctuations in rainfall can cause flooding, which negatively impacts infrastructure, agriculture, health and well-being of the community. Therefore, accurate rainfall forecasting is essential to support informed decision-making.

Objective: The purpose of this study is to identify and build an optimal SARIMA model for rainfall forecasting in Semarang City.

Methods: This study used the Seasonal Autoregressive Integrated Moving Average (SARIMA) method to analyze the monthly rainfall data of Semarang City for the period 2017-2022, because it was able to handle seasonal patterns in the time series data. The best model is determined based on the Akaike Information Criterion (AIC) value, while the accuracy of the prediction is measured using the Mean Absolute Percentage Error (MAPE) value.

Results: Based on the results of the analysis, the best SARIMA model was SARIMA (1,1,0) (0,1,0)12 because it produced the smallest AIC value (121.67) and MAPE of 41.59%. This model is used to predict rainfall from January 2023 to December 2025.

Conclusion: The SARIMA (1,1,0) (0,1,0)12 model is the best model for rainfall forecasting in Semarang City. The results of this study support previous studies that state that the SARIMA method is effective for rainfall data that have high fluctuations and extreme values.

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Published

2024-12-31

How to Cite

Ermawati, A., Amrullah, A., Huda, K., & Haris, M. A. (2024). Implementasi Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) untuk Memprediksi Curah Hujan di Kota Semarang. Jurnal Statistika Dan Komputasi, 3(2), 62–71. https://doi.org/10.32665/statkom.v3i2.3224

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Vol. 3 No. 2 (2024): Jurnal Statistika dan Komputasi

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Abstract View: 40, PDF Download: 21 SIMILARITY INDEX Download: 0