Random Forest Algorithm Implementation for Air Quality Classification in DKI Jakarta Based on ISPU - Khairanisa Salsabila; Tessy Octavia Mukhti (Departemen Statistika Universitas Negeri Padang, 2026)
Air quality is an essential factor that has a direct impact on human health. High concentrations of air pollutants have the potential to cause various health impacts, across short-term and long-term horizons. This study aims to classify air quality in DKI Jakarta using the Air Pollution Standard Index (ISPU) data via the random forest algorithm. The dataset covers a timeframe from 2021 to 2025 and includes air pollutant parameters, namely PM10 and PM2.5 particulate matter, carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), dan ozone (O3). The research method employs a supervised learning approach, in which the data are stratified and evakuated through the implementation of K-Fold Cross Validation (k = 10) to ensure objective and stable model performance. Model performance was measured using Accuracy, Precision, Recall, and F1-Score metrics, along with Confusion Matrix and Feature Importance analyses. It can be seen from the results that the Random Forest model can classify air quality categories with excellent performance, reaching 100% Accuracy on training data and 98.44% on testing data. The Confusion Matrix analysis indicates that most data in each air quality are correctly classified. Furthermore, the Feature Importance analysis reveals PM2.5 that is most influential parameter in determining air quality categories. Therefore, this study indicates that the Random Forest algorithm proves effective for air quality classificati and can function as a decision-support tool for air pollution control and management in DKI Jakarta.