Scholarship Management Information System using Machine Learning Models

Abstract

The paper aims to develop a scholarship management information system using machine learning. This study aims to perform data pre-processing to Pero from trade off classifiers to determine the best model that will be integrated to Scholarship Management Information System module. In order to determine the user acceptance of the system, the researcher used an adopted questionnaire based on ISO/IEC 25010 Software Quality Standards int terms of functional suitability, performance efficiency, usability, reliability, maintainability, security, and portability. Based on the performance metrics the Decision Tree got the highest result of accuracy with a result of 0.987 and this model will be integrated in the Scholarship Management Information System. The results also indicated that the scholarship management information system achieved an overall weighted mean score of 4.57, which corresponds to a verbal interpretation of "Highly Acceptable." The findings imply that the system satisfactorily addresses the functional and operational requirements of its users, thereby offering a dependable framework for the management of scholarship-related activities.