Enhancement of Scholarship Decisions Precision Using Support Vector Machine Algorithm

Abstract

With the increasing number of scholarship applicants, there is a growing need for intelligent systems that support fair and efficient decision-making. This study aims to enhance the scholarship selection process by applying machine learning techniques, specifically the Support Vector Machine (SVM) algorithm. The proposed model analyzes student data according to standardized academic and demographic criteria, classifying applicants as either eligible or ineligible for scholarship awards. The model was trained and evaluated using a real-world dataset, yielding a classification accuracy of 97.28%. Compared to traditional manual methods, the proposed approach enhances fairness, reduces subjectivity, and increases transparency in the distribution of scholarship.