<![CDATA[Locally Generated Revenue (PAD) is the primary source of funding for local governments, with local taxes being the largest component supporting revenue. In Sumbawa Regency, tax target determination is still based on historical potential and realization, resulting in suboptimal accuracy in determining tax targets. This study aims to develop a prediction model for local tax revenue targets using the XGBoost algorithm. Secondary data was obtained from the Sumbawa Regency Regional Revenue Agency (Bapenda), covering various types of local taxes for the 2021–2025 period. The research method uses the Cross Industry Standard Process for Data Mining (CRISP-DM) framework with stages of business understanding, data understanding, preprocessing, modeling, evaluation, and implementation. The evaluation results show an RMSE value of 743,314,988.84 or 743 million, MAPE of 5.34%, and R² of 0.9845, indicating low prediction errors and the model's ability to understand data patterns well. The model is then implemented into a Flask-based web system to support the data input process, model performance, and generate more accurate and data-based predictions of regional tax revenue targets, and has the potential to become a strategic tool in making decisions about determining regional tax targets.]]>
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