Determining Employee Eligibility in Equalizing Staffing Status Using the Naïve Bayes
Rendra Gustriansyah1, Boy Gilang Ramadhan2, Nazori Suhandi3, Ahmad Sanmorino4, John Roni Coyanda5
1Rendra Gustriansyah, Faculty of Computer Science, Universitas Indo Global Mandiri Jl. Jenderal A. Yani, Palembang, Indonesia.
2Boy Gilang Ramadhan, Faculty of Medicine, Universitas Muhammadiyah Palembang Jl. Jenderal Sudirman No. 629, Palembang, Indonesia.
3Nazori Suhandi, Faculty of Computer Science, Universitas Indo Global Mandiri Jl. Jenderal A. Yani, Palembang, Indonesia.
4Ahmad Sanmorino, Faculty of Computer Science, Universitas Indo Global Mandiri Jl. Jenderal A. Yani, Palembang, Indonesia.
5John Roni Coyanda, Faculty of Computer Science, Universitas Indo Global Mandiri Jl. Jenderal A. Yani, Palembang, Indonesia.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 2309-2312 | Volume-9 Issue-1, October 2019 | Retrieval Number: A2627109119/2019©BEIESP | DOI: 10.35940/ijeat.A2627.109119
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The term non-permanent employee first appeared in the rule of law, namely in Act Number 13 Year 2003 concerning Manpower. This Act has an impact on the emergence of clarity about staffing status so that the salaries obtained by employees do not match their workload. Therefore, this study aims to determine employees who are eligible to earn the same income at one of the private universities in Palembang based on university’s strategic plan, namely class, employment status, membership, and education permit using the Naïve Bayes method. The results showed that the highest accuracy of predictive conclusions for non-permanent and permanent employees is 83.33%, while the lowest accuracy value is 50%.
Keywords: Employee Eligibility, Equalizing, Naïve Bayes, Staffing, University