Daily ISPU Classification in DKI Jakarta Based on Gas Pollutant Parameters Using Machine Learning

Abstract

Air pollution is a persistent environmental problem in urban areas, causing serious impacts on public health and environmental quality. Growing transportation activity, industrial output, and population density lead to daily fluctuations in pollutant concentrations. The Air Pollution Standard Index (ISPU) is used as an indicator of air quality based on several pollutant gas parameters, such as PM2.5, PM10, SO2, CO, O3, and NO2. Machine learning approaches can help classify air pollution levels from historical data patterns. This study examines and compares four machine learning algorithms, namely Logistic Regression, Decision Tree, Random Forest, and XGBoost, to classify the daily air pollution level in DKI Jakarta based on ISPU categories. The dataset used is DKI Jakarta air quality data consisting of 5,538 observations, evaluated under two data-splitting scenarios (70:15:15 and 80:10:10) to test model consistency. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The test results show that the 80:10:10 scenario delivers better performance, with XGBoost achieving the highest performance and a test accuracy of 93.68%, outperforming Random Forest, Decision Tree, and Logistic Regression. Based on feature importance analysis, the O3 and PM10 parameters contribute the most to the classification process. These findings indicate that ensemble-learning-based algorithms, particularly XGBoost with a larger training-data proportion, can effectively capture the relationships among pollutant parameters. This approach can support the development of data-driven air quality classification systems, with future research recommended to incorporate weather variables, real-time data streams, further hyperparameter tuning, and deep learning approaches to improve classification accuracy.