<![CDATA[The city of Balikpapan experiences high and fluctuating rainfall intensity, rendering it vulnerable to hydrometeorological disasters such as floods and landslides. Accurate rainfall forecasting is crucial for risk mitigation. This study aims to model and forecast daily rainfall in Balikpapan using the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) method, considering environmental factors. This study utilizes daily data from the January-December 2024 period sourced from NASA POWER, encompassing the variables of rainfall, average temperature, air humidity, and wind speed. The data was analyzed using the Box-Jenkins approach, which includes stationarity tests, parameter estimation, and diagnostic checks. The results indicated that the data was not stationary in variance, necessitating a logarithmic transformation. The best-fit model, identified by the lowest AIC value (582.05), was ARIMAX (1, 0, 1). Analysis of exogenous variables identified that Air Humidity and Wind Speed significantly influence rainfall, whereas Average Temperature does not. The Ljung-Box diagnostic test confirmed that the model's residuals behave as white noise (p-value 0.2662). The model's forecasting evaluation yielded an RMSE of 9.9617. The model proved reasonably effective in capturing the general rainfall patterns, despite limitations in predicting extreme spikes. These findings can contribute a scientific basis to support early warning systems and disaster mitigation policies in Balikpapan.]]>
