Comparative Analysis of the Performance of Random Forest and CatBoost for Air Quality Prediction Based on Meteorological Factor
DOI:
https://doi.org/10.52436/1.jutif.2026.7.3.5412Keywords:
Air Quality, Meteorological Variables, Random Forest, CatBoost, Machine LearningAbstract
Air quality in urban centers such as Tangerang City has become an increasingly urgent issue due to the expansion of industrial activities, rapid population growth, and rising vehicle emissions. As a key city within the Greater Jakarta metropolitan area, Tangerang is highly vulnerable to air pollution caused by human activities and varying meteorological conditions. This study aims to assess the performance of two machine learning algorithms, Random Forest and CatBoost, in predicting air quality in Tangerang under two scenarios: models that incorporate meteorological factors and models that exclude them. The dataset includes concentrations of key air pollutants alongside meteorological variables such as temperature, humidity, and wind speed. Model performance was evaluated using MAE, MSE, RMSE, and R². The findings indicate that both algorithms perform excellently when meteorological variables are included. Random Forest achieved an MAE of 0.0099, MSE of 0.000309, RMSE of 0.0152, and an R² of 0.9931, slightly outperforming CatBoost, which recorded an MAE of 0.0135, MSE of 0.000419, RMSE of 0.0170, and an R² of 0.9907. Excluding meteorological variables decreased accuracy for both models, with Random Forest reaching an R² of 0.9519 and CatBoost 0.9487. These results underscore the importance of temperature, humidity, and wind speed in enhancing predictive accuracy. Notably, this study introduces a comparative evaluation of machine learning models in a unique urban context, providing new insights into how meteorological factors influence air quality predictions. The study contributes to the development of adaptive air quality prediction models, supporting sustainable environmental management planning in Tangerang City.
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