Analisis Klasifikasi Kualitas Udara Di DKI Jakarta Menggunakan Algoritma Decision Tree - Abdul Hadi; Bebi Kurniawan Hura; Moh Azam Abulkhoir; Riski Abimur; Lestin Nurhadia Wati Hulu; Windia Ningsih; Indah Yani Deli (GLOBAL SCIENTS PUBLISHER, 2026)
Air pollution is a serious environmental problem in urban areas, particularly in DKI Jakarta, due to increasing transportation activities, industrial development, and population density. This study aims to classify air quality in DKI Jakarta based on the Air Pollution Standard Index (ISPU) parameters using the Decision Tree algorithm with the CRISP-DM approach. The dataset consists of daily ISPU data from January 2024 to November 2025 obtained from the official Satu Data Jakarta platform, covering PM10, PM2.5, SO₂, CO, O₃, and NO₂ parameters. After data preprocessing, 2,864 records were used for modeling with an 80:20 split between training and testing data, implemented using RapidMiner. The evaluation results show that the Decision Tree model achieved a high classification performance with an accuracy of 99.13%, along with strong precision and recall across all air quality categories. The decision tree structure indicates that PM2.5, PM10, and NO₂ are the most influential attributes in determining air quality levels. These findings demonstrate that the Decision Tree algorithm is effective, accurate, and easily interpretable for air quality classification, and has the potential to support environmental monitoring and decision-making in the public health sector.