MENINGKATKAN PEMILIHAN KARYAWAN TERBAIK DENGAN METODE NAÏVE BAYES DAN K-NEAREST NEIGHBOR (KNN) DALAM SISTEM PENDUKUNG KEPUTUSAN
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.
References
Afifah, & Nur Icha Isti. (2019). Sistem Informasi Penjualan Busana Pengantin Pada Tutut Manten Yogyakarta. 10(1), 250–261.
Alit Suryo Irawan. (2020). Sistem Pendukung Keputusan Epmilihan Jurusan Di Sma Islam Sudirman Ambarawa Menggunakan Metode Analytical Hierarchy Process (Ahp). Stekom.
Arizona, N. D. (2019). Aplikasi Pengolahan Data Anggaran Pendapatan Dan Belanja Desa (Apbdes) Pada Kantor Desa Bakau Kecamatan Jawai Berbasis Web. Cybernetics, 1(2), 105–110.
Hari, T. R. S., & Sumijan, S. (2021). Sistem Pakar Dengan Menggunakan Metode Naive Bayes Dalam Mengidentifikasi Penyakit Karies Pada Gigi Manusia. Jurnal Sistim Informasi Dan Teknologi, 233–238. Https://Doi.Org/10.37034/Jsisfotek.V3i4.71
Lan Yu, Guoqing Chen, & Andy Koronios. (2018). Application And Comparison Of Classification Techniques In Controlling Credit Risk. 111–145.
Leidiyana, H. (2013). Penerapan Algoritma K-Nearest Neighbor Untuk Penentuan Resiko Kredit Kepemilikan Kendaraan Bemotor. In Jurnal Penelitian Ilmu Komputer, System Embedded & Logic (Vol. 1, Issue 1).
Murdianto Handri, Marisa Khairina Dina, & Hatta Heliza Rahmania. (2022). Sistem Pendukung Keputusan Pemilihan Karyawan Terbaik Per Triwulan Pt.Cahaya Fajar Kaltim Pltu Embalut Tanjung Batu Menggunakan Metode Simple Additive Weighting. Seminar Ilmu Komputer Dan Teknologi Informasi, 1(1), 24–29. Https://Doi.Org/10.31219/Osf.Io/J4yva
Nurmayanti Desti, Haryanti Tuti, Septiana Laila, & Nurdiani Siti. (2022). Penerapan Metode Profile Matching Untuk Sistem Penunjang Keputusan Pemilihan Karyawan Terbaik. Satin - Sains Dan Teknologi Informasi, 8(1), 118–128. Https://Doi.Org/10.33372/Stn.V8i1.838
Sari, I. P., Jannah, A., Meuraxa, A. M., Syahfitri, A., & Omar, R. (2022). Perancangan Sistem Informasi Penginputan Database Mahasiswa Berbasis Web. Hello World Jurnal Ilmu Komputer, 1(2), 106–110. Https://Doi.Org/10.56211/Helloworld.V1i2.57
Septiani, P., Pratiwi, I., Ghofar Rohman, M., & Sholihin, M. (2023). Sistem Pakar Penyakit Telinga Menggunakan Metode Naïve Bayes. In Generation Journal (Vol. 7, Issue 2).
