<![CDATA[The KIP Kuliah program aims to improve access to higher education for students from underprivileged families. The scholarship selection process, which is often conducted manually, tends to be inefficient and prone to subjectivity. This study aims to develop a classification model using the Decision Tree C4.5 algorithm based on a data mining approach. The dataset includes parents’ income, parents’ occupation and education, number of dependents, home ownership and condition, electricity capacity, house size, and achievement data. The research stages include data collection, preprocessing, model development, and evaluation using a confusion matrix and performance metrics such as accuracy, precision, recall, and f1-score. The results show that the model achieves an accuracy of 98%. Parents’ income and number of dependents are the most influential factors in determining eligibility. The resulting model has a simple structure, making it easy to interpret and useful for supporting a more objective and efficient selection process.]]>
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