Air Quality Index Classification: Feature Selection for Improved Accuracy with Multinomial Logistic Regression
DOI:
https://doi.org/10.52436/1.jutif.2025.6.5.5155Keywords:
Air quality index, Classification, Data mining, K-fold cross validation, Multinomial logistic regressionAbstract
Air pollution is a major public health concern, creating the need for accurate and interpretable Air Quality Index (AQI) classification models. This study aims to classify AQI into three categories—Good, Moderate, and Unhealthy—using Multinomial Logistic Regression (MLR) with feature selection. The dataset, obtained from public monitoring stations in Jakarta between 2021 and 2024, initially contained 4,620 daily records. After cleaning and outlier removal, 3,586 valid samples remained, from which 900 balanced records (300 per class) were selected for modeling. Key features included PM₁₀, PM₂.₅, SO₂, CO, O₃, and NO₂, which were standardized using Max Normalization to ensure uniform feature scaling. The classification process applied k-fold cross-validation (k = 2–5), and performance was assessed using accuracy and Macro F1-score. Results show that including PM₂.₅ improves performance by about 10%, with the best outcome at k = 5 (accuracy = 91.67%, Macro F1 = 91.45%). These findings confirm PM₂.₅ as a decisive feature for AQI prediction and demonstrate that MLR provides a lightweight, transparent, and computationally efficient solution. Beyond environmental health, the contribution of this work lies in advancing data-driven decision support systems in Informatics, particularly for real-time monitoring and policy applications.
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Copyright (c) 2025 Rizky Caesar Irjayana, Abdul Fadlil, Rusydi Umar
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