OPTIMIZATION OF NAÏVE BAYES BASED ON GENETIC ALGORITHM FOR PERFORMANCE EVALUATION OF LECTURERS OF PGRI WIRANEGARA UNIVERSITY

Sapto Hadi Riono

doi:10.32764/saintekbu.v14i02.3515

Authors

Sapto Hadi Riono Universitas PGRI Wiranegara

DOI:

https://doi.org/10.32764/saintekbu.v14i02.3515

Keywords:

Performance Evaluation Of Lecturers, Naïve Bayes, Genetic Algorithm, feature selection

Abstract

The evaluation of educators' performance, particularly that of professors, is crucial for maintaining the high quality of instruction at the college. This study employed Bayes Methods as the basis for performance assessment, using the criteria set forth by the quality assurance unit's standards of conformity. The results of the Bayes Method will aid the institution in evaluating professors' performance, with the goal of enhancing their skills and serving as a decision-making tool. A Genetic Algorithm (GA) was proposed to optimize the Naïve Bayes parameter value and improve accuracy. The results showed that the accuracy of the Naive Bayes was 89.93%, while the accuracy of the Naive Bayes with Genetic Algorithm was 95%, indicating that the latter method is more effective

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