Authors :
Eliza B. Ayo; Lani E. Sakay; Anna Liza O. Villanueva; Rosalina R. Pangilina; Celia L.Verano
Volume/Issue :
Volume 9 - 2024, Issue 2 - February
Google Scholar :
https://tinyurl.com/86pybwzr
Scribd :
https://tinyurl.com/43nzja43
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24FEB1647
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.
Keywords :
Qualification Prediction, Machine Learning, Naïve Bayes Algorithm.
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.
Keywords :
Qualification Prediction, Machine Learning, Naïve Bayes Algorithm.
