<![CDATA[Scholarships are educational financial aids granted to students based on specific criteria such as economic background, academic achievement, or a combination of both. These programs play a crucial role in preventing school dropouts and promoting educational equity and access, especially for junior high school students from underprivileged families. This study aims to develop a classification system for determining scholarship eligibility using data mining methods, particularly the C4.5 algorithm, to ensure that the selection process for scholarship recipients is objective, efficient, and transparent.The research was conducted quantitatively by analyzing student data with economic and academic attributes through stages of data selection, pre-processing, transformation, and result evaluation, utilizing the RapidMiner software. Implementation results show that the C4.5 classification model achieves a high level of accuracy in identifying eligible scholarship recipients, as measured by metrics such as accuracy, precision, and recall. Consequently, the developed system can minimize selection errors and improve the quality of decision-making. Overall, applying the C4.5 algorithm significantly enhances the effectiveness and efficiency of the scholarship selection process, supporting fairness, transparency, and accountability. Furthermore, it opens opportunities for future development by integrating more diverse data and additional machine learning methods to further optimize scholarship selection.The expected result include an Accuracy of 99.80%, Precision of 98.26%, and Recall of 100.00%. These outcomes support more accurate and targeted scholarship decision, ensure transparency and accountability, and open opportunities for future development through data integration to optimize scholarship selection.]]>
