PREDIKSI PEMAKAIAN AIR BULANAN DI PDAM KECAMATAN TAMALATE MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA)
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Abstract
Water consumption forecasting is a crucial aspect of efficient water resource management, particularly in urban areas with increasing demand. This study aims to predict the monthly water usage volume at the PDAM of Tamalate District using the Autoregressive Integrated Moving Average (ARIMA) method. The dataset consists of historical water usage data from January 2022 to December 2024, totaling 36 monthly observations. The analysis process includes stationarity testing using the Augmented Dickey-Fuller (ADF) test, model parameter identification through ACF and PACF plots, and performance evaluation using MAE, RMSE, and MAPE metrics. The results show that the best-performing model is ARIMA, which demonstrates high prediction accuracy, with a MAE of 26,049.80 m³, RMSE of 37,459.00 m³, and MAPE of 4.12%. This model is capable of generating predictions close to actual values and can be relied upon as a basis for PDAM’s water distribution planning. It is expected that this research will contribute to data-driven decision-making and support digital transformation in the public service sector.
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