EMPLOYEE TURNOVER CLASSIFICATION USING PSO-BASED NAÏVE BAYES AND NAÏVE BAYES ALGORITHM IN PT. MASTERSYSTEM INFOTAMA

Endang Sri  Palupi

doi:10.34288/jri.v3i3.83

Authors

Endang Sri Palupi Universitas Bina Sarana Informatika

(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v3i3.83

Keywords:

Algoritma Naïve Bayes, Particle Swarm Optimization, Turnover

Abstract

Turnover occurs because many employees leave and new employees enter, so the turnover in and out of employees is quite high, therefore turnover can be controlled with a strategy to increase employee engagement. PT. Mastersystem Infotama is a System Integrator company or better known as a fairly large IT company with a total of approximately 600 employees. Turnover is high enough to make some divisions lack human resources, and the human capital management division is quite difficult to recruit employees to find candidates with various criteria that must be available in a short time. Competition in the IT world is quite tight both within companies and employees with good experience and abilities. Especially the sales department that holds a database of potential customers, and the engineer section that already has a certificate of expertise that is widely used in the IT business world. Therefore, it is necessary to classify what factors make employee turnover high by using the Naïve Bayes and Naïve Bayes algorithms based on Particle Swarm Optimization, so that they can be used as material for internal evaluation to increase employee engagement. The results of this study, classification using the Naïve Bayes algorithm, has an accuracy of 79.17%, while the classification using the Naïve Bayes algorithm based on Particle Swarm Optimization is 94.17%.

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