<![CDATA[Scholarship programs are one of the efforts made by universities to improve educational quality while supporting students with limited economic conditions. The scholarship selection process generally considers various socio-economic factors, including parents' income, number of family dependents, Grade Point Average (GPA), ownership of the Indonesia Smart Card (KIP), and housing status. In practice, the selection process is still largely performed manually, which is time-consuming, prone to recording errors, and potentially less objective because it depends on the subjective judgment of the staff involved. This condition highlights the need for a computational approach that can support faster, more consistent, and measurable decision-making. This study aims to implement the Naive Bayes algorithm, a probability-based classification method that works based on Bayes' Theorem with the assumption of conditional independence among attributes, to classify scholarship eligibility based on students' socio-economic data. The research method consists of five main stages: data collection, data preprocessing (handling missing values, duplicate records, and categorical attribute transformation), dataset splitting using the Hold-Out Validation method with a composition of 80% training data and 20% testing data, model training using the Naive Bayes algorithm implemented with the Scikit-learn library, and performance evaluation using a Confusion Matrix. The dataset consists of 200 student records with seven attributes, namely parents' income, number of dependents, GPA, housing status, KIP ownership, semester level, and eligibility status. The results show that the Naive Bayes algorithm is able to classify scholarship eligibility with an Accuracy of 92.50%, Precision of 95.65%, Recall of 91.67%, and F1-Score of 93.62%. These results confirm that the Naive Bayes algorithm can be used as a decision support method to make the scholarship selection process faster, more objective, and more efficient than manual selection.]]>
