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All Journal Journal of Research and Publication Innovation
Alfakhriy Aqil Imadani
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Computer Science & IT Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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MENINGKATKAN PEMILIHAN KARYAWAN TERBAIK DENGAN METODE NAÏVE BAYES DAN K-NEAREST NEIGHBOR (KNN) DALAM SISTEM PENDUKUNG KEPUTUSAN Alfakhriy Aqil Imadani; Arie Gunawan
Journal of Research and Publication Innovation Vol 2 No 4 (2024): OCTOBER
Publisher : Journal of Research and Publication Innovation

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Abstract

Selection of the best employees is an important process in human resource management that requires objective evaluation of various criteria. Decision Support Systems (DSS) are effective tools to assist this process by utilizing historical data and specific algorithms. This research aims to develop a SPK that integrates two classification methods, namely Naïve Bayes and K-Nearest Neighbor (KNN), to determine the best employees based on criteria such as attendance, discipline, responsibility, loyalty, attitude and target achievement. The Naïve Bayes method is used to determine the probability of an employee being the best based on certain variables, while KNN groups employee data based on proximity to historical data. The test results show that the Naïve Bayes method achieves an accuracy level of 87.5%, while the KNN method achieves an accuracy of 93.75%. The implementation of this system is expected to help companies, especially HRD, in selecting the best employees more quickly and accurately.
Co-Authors Arie Gunawan