Perbandingan Metode K-NN dan SVM Berdasarkan Kinerja Pegawai

Authors

  • Sinarring Azi Laga Universitas Hayam Wuruk Perbanas, Surabaya

DOI:

https://doi.org/10.30865/json.v4i3.5816

Keywords:

Classification, K-NN, SVM, Comparison, Performance

Abstract

Limited qualified human resources cause employees not to do the job in accordance with the company's operational standards properly and correctly. At this time PT. XYZ does not have tools to identify employee performance, therefore researchers conduct research to assist PT. XYZ in classifying employee performance. The methods used in this study were K-NN and SVM with a sample of 873 PT. XYZ employee data. Based on the trials conducted, the K-NN method has the highest accuracy rate of 90.13%, 91% precision rate, and 98.95% recall rate. The most optimal number of neighbors (k value) for the K-NN method is 5 with an accuracy rate of 88.35%.

References

K. Hadi and B. N. Hidayah, “PENGARUH BEBAN KERJA, LINGKUNGAN KERJA DAN KOMPENSASI TERHADAP KINERJA KARYAWAN PADA PT. KALIMIAS BINTANG PRATAMA CABANG LOMBOK TENGAH,†VALID Jurnal Ilmiah, pp. 215–223, 2019.

A. Amellya, F. Fitriasuri, and E. Elpanso, “Pengaruh Kompetensi dan Motivasi terhadap Kinerja Pegawai pada Badan Pengelola Keuangan dan Aset Daerah Kabupaten Banyuasin,†2022.

Hasmin and J. Nurung, MANAJEMEN SUMBER DAYA MANUSIA, 2021st ed. 2021. doi: 10.31237/osf.io/yvpue.

B. HARALAYYA Hod and A. Professor, “Employee Performance Appraisal at Sri Veerabhadreshwar Motors Bidar,†2022.

A. S. Lombu, S. Hidayat, and A. F. Hidayatullah, “Pemodelan Klasifikasi Gaji Menggunakan Support Vector Machine,†Journal of Computer System and Informatics (JoSYC), vol. 3, no. 4, pp. 363–370, Sep. 2022, doi: 10.47065/josyc.v3i4.2137.

A. Rahmat, K. Auliasari, and Y. A. Pranoto, “IMPLEMENTASI METODE K-NEAREST NEIGHBOR (KNN) UNTUK SELEKSI CALON KARYAWAN BARU (Studi Kasus : BFI Finance Surabaya),†2020.

I. Setiari, “PENGARUH SISTEM IMBALAN DAN KEBIJAKAN UPAH TERHADAP PRESTASI KERJA PEGAWAI KANTOR DEPARTEMEN AGAMA KOTA BANJAR,†Jurnal Media Teknologi, vol. 09, no. 01, 2022.

R. Umar, I. Riadi, and Purwono, “Klasifikasi Kinerja Programmer pada Aktivitas Media Sosial dengan Metode Stochastic Gradient Descent,†JOINTECS (Journal of Information Technology and Computer Science), vol. 3, no. 1, pp. 55–60, 2020.

R. Umar, I. Riadi, U. Ahmad Dahlan Yogyakarta, J. D. Soepomo, and K. Umbulharjo, “Klasifikasi Kinerja Programmer pada Aktivitas Media Sosial dengan Metode Support Vector Machines,†CYBERNETICS, vol. 4, no. 01, pp. 32–40, 2020.

M. R. Alghifari and A. P. Wibowo, “Penerapan Metode K-Nearest Neighbor Untuk Klasifikasi Kinerja Satpam Berbasis Web,†2019.

P. R. Sihombing and O. P. Hendarsin, “Perbandingan Metode Artificial Neural Network (ANN) dan Support Vector Machine (SVM) untuk Klasifikasi Kinerja Perusahaan Daerah Air Minum (PDAM) di Indonesiaâ€.

L. Iryani, “PENERAPAN MACHINE LEARNING DALAM KLASIFIKASI KINERJA PEGAWAI PT X,†Jurnal Informanika, vol. 09, no. 01, 2023, [Online]. Available: https://3.bp.blogspot.com/-

I. Hajiali, A. M. Fara Kessi, B. Budiandriani, E. Prihatin, M. M. Sufri, and A. Sudirman, “Determination of Work Motivation, Leadership Style, Employee Competence on Job Satisfaction and Employee Performance,†Golden Ratio of Human Resource Management, vol. 2, no. 1, pp. 57–69, Feb. 2022, doi: 10.52970/grhrm.v2i1.160.

Fauziah, M. A. Tiro, and Ruliana, “Comparison of k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) Methods for Classification of Poverty Data in Papua,†ARRUS Journal of Mathematics and Applied Science, vol. 2, no. 2, pp. 83–91, Mar. 2022, doi: 10.35877/mathscience741.

I. G. Hendrayana, D. G. H. Divayana, and M. W. A. Kesiman, “KOMPARASI METODE SVM, K-NN DAN NBC PADA ANALISIS SENTIMEN,†Jurnal Indonesia : Manajemen Informatika dan Komunikasi, vol. 4, no. 1, pp. 191–198, Jan. 2023, doi: 10.35870/jimik.v4i1.157.

A. P. Wibawa, M. Guntur, A. Purnama, M. Fathony Akbar, and F. A. Dwiyanto, “Metode-metode Klasifikasi,†Prosiding Seminar Ilmu Komputer dan Teknologi Informasi, vol. 3, no. 1, 2018.

R. Djutalov, “ANALISIS SUKSESI SDM MENGGUNAKAN ALGORITMA KLASIFIKASI K-NEAREST NEIGHBOUR DAN ALGORITMA CLUSTERING K-MEANS ( STUDI KASUS : MABES POLRI),†Jurnal Ilmu Komputer JIK, vol. V, no. 1, pp. 24–29, 2022.

I. Melani, B. Priyatna, F. Nurapriani, and S. S. Hilabi, “Implementasi Metode K-Means Clustering Pada Penilaian Kinerja Karyawan PT Kopetri Citra Abadi,†Jurnal Informatika dan Teknologi Informasi, vol. 8, no. 1, pp. 24–30, 2023, [Online]. Available: http://e-journal.janabadra.ac.id/

B. B. Aji, “Sistem Penilaian Kinerja Berbasis Sasaran Kinerja Pegawai (SKP) di Lingkungan Sekretariat Daerah Kota Banjarbaru,†Journal on Education, vol. 05, no. 01, pp. 1057–1064, 2022.

A. N. Arifah, J. Suprijadi, and I. Ginanjar, “Klasifikasi Rumpun Jabatan ASN Berdasarkan Riwayat Pelatihan Menggunakan Multiclass Support Vector Machine,†Jurnal Statistika Teori dan Aplikasi, vol. 1, no. 1, pp. 191–197, 2022, [Online]. Available: http://prosiding.statistics.unpad.ac.id

D. Lorinda and W. Saputro, “Klasifikasi Sistem Pendukung Keputusan Pemilihan Pegawai Terbaik Menggunakkan Metode Algoritma C4.5 (Studi Kasus: Subdit 1 Dit Tipidum Bareskrim Polri Jakarta,†Jurnal Pendidikan dan Konselimh, vol. 4, no. 5, pp. 1080–1093, 2022.

Downloads

Published

2023-03-31

How to Cite

Laga, S. A. (2023). Perbandingan Metode K-NN dan SVM Berdasarkan Kinerja Pegawai. Jurnal Sistem Komputer Dan Informatika (JSON), 4(3), 420–425. https://doi.org/10.30865/json.v4i3.5816

Issue

Section

Articles