Purpose: This study aimed to develop a machine learning-based classification model for predicting the Air Pollution Standard Index (ISPU/AQI) categories in DKI Jakarta using the Support Vector Machine (SVM) algorithm. This study specifically addresses the challenge of accurate multiclass air quality classification under real-world urban conditions characterized by high pollution variability and imbalanced class distributions.

Research Methodology: This study employed a quantitative research design using secondary time-series data comprising 1,825 daily observations from the Satu Data Jakarta portal (satudata.jakarta.go.id), covering the period from February to November 2023. Six pollutant parameters, namely PM2.5, PM10, CO, SO?, NO?, and O?, served as predictor features. Data preprocessing included missing value imputation, duplicate removal, label encoding, and min-max normalization. The SVM classifier with a Radial Basis Function (RBF) kernel was implemented using Python’s Scikit-learn library on Google Colaboratory. Hyperparameter optimization was conducted via GridSearchCV with stratified K-fold cross-validation, and the model was evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis.

Results: The optimized SVM model achieved an overall classification accuracy of 96.1% on the held out test set. The macro-average F1-scores were 86%, 98 %, and 93% for the ‘Good, ’ ‘Moderate, ’ and ‘Unhealthy’ categories, respectively. Class imbalance was identified as a primary challenge, with minority classes (Very Unhealthy, Hazardous) underrepresented in the dataset, contributing to differential model performance across categories.

Conclusions: The SVM-RBF model demonstrated high predictive accuracy for air quality classification in urban tropical environments, confirming its applicability as a foundation for automated real-time air quality monitoring systems. The results establish a replicable methodological framework for similar studies in other Indonesian metropolitan regions.

Limitations: The dataset is geographically restricted to DKI Jakarta and temporally limited to a ten-month period, constraining generalizability. Class imbalance for extreme pollution categories (Very Unhealthy, Hazardous) limits the model reliability for rare but critical events.

Contributions: This study contributes a validated SVM-based classification pipeline for AQI prediction in a tropical megacity context, providing methodological guidance for environmental informatics researchers and urban policy practitioners in developing countries seeking scalable, data-driven air-quality management tools.