Klasterisasi kinerja karyawan menggunakan algoritma fuzzy c-means

Authors

  • Martin Martin
  • Yessica Nataliani Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

DOI:

https://doi.org/10.24246/aiti.v17i2.118-129

Keywords:

employees' performance, clustering, fuzzy c-means

Abstract

Reward and punishment are needed for assessting employees’ performace. Employee grouping based on their performance is one of several ways to enhance employees’ performance. This research discusses about grouping employess based on their performance using Fuzzy C-Means. Result from assessment comes from the total of each criteria that contains of presence, discipline, and task duration. Three groups of employees are formed, which are good, moderate, and bad. From 13 employees, 10 of them are in the good criteria, one is in moderate criteria, and two are in the bad one. We also use different values of fuzzy exponent to get the clustering results. The values 1.5 and 2 of fuzzy exponents give the same clustering results with the result from manager. Therefore, grouping with FCM could be used to cluster employees based on their performance.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

A. Aidina, Ristyawan, Kusrini, Sunyoto, “Pemanfaatan Algoritma FCM Dalam Pengelompokan Kinerja Akademik Mahasiswa,” Konf. Nas. Sist. Inform. 2015, pp. 431–436, 2015.

F. Wulandari and R. Setiawan, “Clustering Karyawan Berdasarkan Kinerja Dengan Menggunakan Logika Fuzzy C-Mean,” J. Penelit. Univ. Islam Negeri Syarif Kasim, Riau, pp. 1–7, 2010.

F. Nasari and C. J. M. Sianturi, “Penerapan Algoritma K-Means Clustering Untuk Pengelompokkan Penyebaran Diare Di Kabupaten Langkat,” CogITo Smart J., vol. 2, no. 2, p. 108, 2016, doi: 10.31154/cogito.v2i2.19.108-119.

N. Agustina and P. Prihandoko, “Perbandingan Algoritma K-Means dengan Fuzzy C_Means Untuk Clustering Tingkat Kedisiplinan Kinerja Karyawan (Studi Kasus: Sekolah Tinggi Teknologi Bandung),” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 2, no. 3, pp. 621–626, 2018, doi: 10.29207/resti.v2i3.492.

A. K. Wijaya, “Implementasi Data Mining dengan Algoritma Fuzzy C - Means Studi Kasus Penjualan di UD Subur Baru,” Jur. Tek. Inform. FASILKOM UDINUS, pp. 1–8, 2014.

A. A. Khoiruddin, “Menentukan nilai akhir kuliah dengan fuzzy c-means,” Semin. Nas. Sist. dan Inform., vol. 1965, no. November, pp. 232–238, 2007.

J. Tamaela, E. Sediyono, and A. Setiawan, “Cluster Analysis Menggunakan Algoritma Fuzzy C-means dan K-means Untuk Klasterisasi dan Pemetaan Lahan Pertanian di Minahasa Tenggara,” J. Buana Inform., vol. 8, no. 3, pp. 151–160, 2017, doi: 10.24002/jbi.v8i3.1317.

R. A. M. S. D. Yuhandri, “PERBANDINGAN ALGORITMA K-MEANS CLUSTERING DENGAN FUZZY C- MEANS DALAM MENGUKUR TINGKAT KEPUASAN TERHADAP TELEVISI Latar Belakang Masalah Media Televisi Dakwah Surau TV merupakan sebuah media penyiaran yang menyajikan siaran seputar Agama Islam . Media ini,” vol. 3, no. 1, pp. 10–21, 2018.

Downloads

Published

2021-02-23

How to Cite

[1]
M. Martin and Y. Nataliani, “Klasterisasi kinerja karyawan menggunakan algoritma fuzzy c-means”, AITI, vol. 17, no. 2, pp. 118–129, Feb. 2021.

Issue

Section

Articles