<![CDATA[PBB-P2 Tax Revenue plays an essential role in regional finance, but managing receivables and analyzing taxpayer compliance levels still face many challenges. Business Intelligence (BI) technologies such as Apache Superset are often used for interactive data visualization. Still, they have limitations in advanced analysis, especially the application of machine learning algorithms such as K-Means for data clustering. This research aims to overcome the limitations of Apache Superset by developing an external application-based solution using the Java programming language and the SMILE library. This application is designed to cluster the level of taxpayer compliance in a batch process, with the results stored in the MySQL database. The clustered data is then visualized using Apache Superset. The results show that integrating these external applications can improve the efficiency of data analysis by utilizing more complex clustering algorithms. Visualization of clustering results also allows for more effective management of PBB-P2. This approach not only expands the capabilities of Apache Superset but also contributes to supporting data-driven tax revenue optimization strategies. This research opens up further opportunities for the integration of BI tools with machine learning algorithms in monitoring and managing complex data in the tax sector]]>
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