<![CDATA[Air pollution is a serious problem that has an impact on the health and quality of life of people in metropolitan cities like Jakarta. To overcome these challenges, an accurate and reliable air quality prediction method is needed. Extreme Gradient Boosting (XGBoost) is a machine learning algorithm that excels at handling non-linear and complex data, making it ideal for modeling air quality. This study aims to develop an air quality prediction model in Jakarta using XGBoost, utilizing pollutant data that builds an Air Quality Index (AQI) obtained through a data mining process using the Earth Engine Code Editor.Model evaluation was carried out using RMSE, MAE, R2, and RSE metrics, which showed that XGBoost provided excellent prediction performance. The feature importance analysis identified SO2, PM2.5, and PM10 as the main factors affecting air quality in Jakarta. The results of this study are expected to support the government in making air pollution mitigation policies and developing an effective early warning system to improve the quality of life of the community.]]>
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