<![CDATA[The digital economy is experiencing quite rapid development, not to mention the e-commerce sector which is showing growth. From 2025 to 2030 it is projected that there will be a three-fold increase, mainly from the e-commerce sector. A number of transactions and data are abundant, causing DGT to have difficulty processing data. In addition, determining the involvement of e-commerce actors who are not easily detected is a challenge in itself. Based on these problems, this study raises the application of data mining in taxation to analyze the potential of e-commerce through comparative studies. This study uses a qualitative descriptive approach with literature study and interview methods through the acquisition of primary data from informants and secondary data through relevant literature sources. This study uses content analysis techniques. The results of the study show that Indonesia needs to adopt a data mining system implemented by several countries based on benchmark analysis. Comparison and effectiveness of data mining, especially in preventing, detecting, and overcoming tax evasion behavior by taxpayers. In addition, reviewing the potential to be implemented in Indonesia is very large. Application of data in large sets will be processed using machine learning, random forest, artificial intelligence, and data mining. Based on analysis with other countries, this method minimizes the number of problems in computerized systems]]>
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