Selection of the Best Employees Using the Copras Method at PT. Gosyen Retail Indonesia

Authors

  • Desvita Ramanda Nur Widisetyo Universitas Nusa Mandiri
  • Achmad Rifai Universitas Bina Sarana Informatika
  • Irmawati Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.59934/jaiea.v3i3.499

Keywords:

Decision Support System, COPRAS, Employee Selection

Abstract

The most important asset of a company is its human resources. So that the quality and existence of its people need to be maintained. Therefore, the company has an obligation to always maintain the quality of its employees. One way that companies can maintain the quality of their employees is by giving rewards for their success at work. Rewards are certainly not given to all employees but are given to someone or several employees for their achievements. The purpose of this research is to find the best employee by being influenced by several criteria, namely performance, work attitude, teamwork, accuracy and discipline. The research method used is the Copras (Complex Proportional Assessment) method. The data sources used are secondary type data sources by reviewing articles in journals and secondary data books in supporting this research, all of which are in accordance with the research objectives or research relevance. The results of this study obtained the best employee with alternative U22 with a final result of 100 on behalf of Tasya Qanita.

 

Downloads

Download data is not yet available.

References

D. Septiani and F. B. Siahaan, “Sistem Penunjang Keputusan Pemilihan Karyawan Berprestasi Dengan Metode Analitical Hierarchy Process (Ahp) Pada Pt. Ichiya Indonesia,” J. Tek. Komput., vol. 3, no. 1, pp. 1–8, 2017.

T. Utama, Ivone, W. P. Han, B. Berluidaham, and Megawati, “Penilaian Kinerja Karyawan Pada PT. Dinamika Lubsindo Utama Medan,” Semin. Nas. Teknol. Komput. Sains, pp. 96–98, 2019.

J. Hutagalung and M. T. Indah R, “Pemilihan Dosen Penguji Skripsi Menggunakan Metode ARAS, COPRAS dan WASPAS,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 10, no. 3, pp. 354–367, 2021, doi: 10.32736/sisfokom.v10i3.1240.

P. COPRASDalam Penentuan Kepolisian Sektor Terbaik, G. Ginting, S. Alvita, A. Karim, M. Syahrizal, and N. Khairani Daulay, “Penerapan Complex Proportional Assessment (COPRAS) Dalam Penentuan Kepolisian Sektor Terbaik,” J. Sains Komput. Inform. (J-SAKTI, vol. 4, no. 2, pp. 616–631, 2020.

Suma Dia Syahwani, Y. Maulita, and M. A. Syari, “Application Of The Profile Matching Method In The Selection Of New Students For Batak Karo Bridal Makeup Skills In The PKK Program”, j. of artif. intell. and eng. appl., vol. 3, no. 1, pp. 228–233, Oct. 2023.

I. Muhammad, R. Dahlia, Muhammad Ifan Rifani Ihsan, Lisnawanty, and Rabiatus Sa’adah, “Performance Analysis of Ensemble Learning and Feature Selection Methods in Loan Approval Prediction at Banks”, j. of artif. intell. and eng. appl., vol. 3, no. 2, pp. 557–564, Feb. 2024.

K. Muhammad Abdul Khalid, “Selection of the Best Village Crop Potential Using the Multi-Attribute Border Approximation Area Comparison (MABAC) Method”, j. of artif. intell. and eng. appl., vol. 3, no. 1, pp. 394–407, Oct. 2023.

P. Lishayani, R. Buaton, and T. R. Pasaribu, “Determining The Selection Of Departments At Abdi Negara Vocational School Using The Additive Ratio Assessment (Aras) Method”, j. of artif. intell. and eng. appl., vol. 3, no. 1, pp. 31–37, Oct. 2023.

Rahmawati, R. Saragih, and M. A. Syari, “Implementation of the Smart Method in Selection of Contraceptive Devices in Couples of Childbearing Age Case Study: Datar City Health Center”, j. of artif. intell. and eng. appl., vol. 3, no. 1, pp. 514–520, Oct. 2023.

U. Anzar, “Employee Selection Decision Support System The Best Marketing at SMK Dwiwarna Medan Using the Simple Additive Weighting Method”, j. of artif. intell. and eng. appl., vol. 3, no. 1, pp. 440–447, Oct. 2023.

Downloads

Published

2024-06-15

How to Cite

Widisetyo, D. R. N., Rifai, A., & Irmawati. (2024). Selection of the Best Employees Using the Copras Method at PT. Gosyen Retail Indonesia. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 3(3), 719–733. https://doi.org/10.59934/jaiea.v3i3.499

Issue

Section

Articles