Development of Faculty Qualification Analysis System using Naive Bayes Algorithm

Abstract

Qualification prediction is a crucial process in determining whether an applicant is qualified for a particular position. However, traditional methods of evaluation often rely on the experience and intuition of the evaluator, which may not always be accurate. This study proposed the use of a supervised machine learning approach, specifically the Naïve Bayes algorithm, to predict faculty qualification based on a labeled dataset. The developed Faculty Qualification Analysis System for Perpetual Help College of Manila would allow users to input appropriate test data and generate results of qualified or not qualified. The system’s effectiveness and acceptance had 4.3 and 4.4 ratings with verbal interpretation of very high and strongly acceptable. The results of this study demonstrated the potential of machine learning algorithms to improve the accuracy and efficiency of qualification prediction processes in educational institutions.