Evaluating Random Forest Regression for Air Quality Prediction - Izabi, Muh. Basyar; Annas, Suwardi; Ahmar, Ansari Saleh (Universitas Bumigora, 2026)
Air pollution is a growing environmental issue in Makassar due to rapid urban development and increasing transportation activity. This study aims to model and predict air pollutant concentrations using the Random Forest (RF) regression method. The data consist of daily PM2.5, PM10, CO, NO2, SO2, and O3 measurements from September 2024 to September 2025, totaling 395 observations. Missing values (14.05%) were addressed using a hybrid approach combining linear interpolation and multiple linear regression. The RF model was trained under two data-split scenarios (70:30 and 80:20) and evaluated using SMAPE, RMSE, MAE, and R2. The results show that the 80:20 configuration provides the best predictive accuracy. CO and O3 yield the most accurate predictions with SMAPE values of 9.75% and 10.87%, and R2 of 0.973 and 0.964, respectively. PM2.5 and PM10 also show strong performance, with R2 values above 0.84. These results indicate that the RF model effectively captures pollutant variability and provides reliable forecasts. Overall, Random Forest has been shown to be a robust and accurate method for predicting air quality in Makassar, supporting environmental monitoring and early warning systems. Despite its strong performance, this study is limited to two data-partition schemes and does not incorporate temporal deep-learning architectures. Future studies may investigate hybrid ensembles or deep learning approaches to determine whether incorporating sequential modeling further enhances predictive stability.