Extreme Gradient Boosting Performance on Air Pollution Level Classification in DKI Jakarta - Harni Kusniyati,; Yerico Immanuel Rumahorbo,; Raka Yusuf, (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025)
Air pollution is a major challenge in metropolitan cities like Jakarta. This study evaluates the performance of the Extreme Gradient Boosting (XGBoost) algorithm in classifying air pollution levels using the Air Pollution Standard Index (ISPU) dataset from Satu Data Jakarta. The research stages include data cleaning, selecting relevant features, and applying XGBoost to detect patterns that affect air quality. Experimental results show that XGBoost achieves high performance with an accuracy of 90.22%, a precision of 90.55%, and a recall of 90.22%. These findings confirm the effectiveness of XGBoost in identifying air pollution levels, while providing important contributions to air quality monitoring and the development of more appropriate pollution mitigation policies in Jakarta.
Keywords: Air pollution; Air pollution classification; XGBoost; Jakarta; ISPU