Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Informatika Teknologi dan Sains (Jinteks)
Mudakir
Universitas Pelita Bangsa
Computer Science & IT Control & Systems Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
PENERAPAN DATA MINING UNTUK KLASIFIKASI PENGANGKATAN KARYAWAN MENGGUNAKAN ALGORITMA K-MEANS Mudakir; Ahmad Turmudi Zy; Aswan S. Sunge
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 5 No 3 (2023): EDISI 17
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v5i3.3369

Abstract

In this ever-evolving digital era, technology has become one of the main drivers of innovation, efficiency and competitive advantage for companies in various sectors. Appointment of prospective employees is an agenda carried out by the company where for a contract employee who has served during the contract agreement period. PT. Karya Bahana Unigam, which is engaged in the automotive sector, has approximately 500 employees, so it is difficult for the company to carry out the selection process for hiring employees who are still eligible and meet the requirements. K-Means is a data clustering method that tries to partition existing data into one or more clusters or groups. K-Means is used to group employee data based on certain criteria, while the Davies Bouldin Index is used to measure the quality of the clustering results. Of the 128 employee assessment datasets, tests were carried out by determining 2 clusters and validation was tested with the Davies Bouldin Index. And the resulting -2,803. Based on the results obtained, it shows that the k-means algorithm can be implemented in grouping for hiring employees with fairly good validation results.
Co-Authors Ahmad Turmudi Zy Aswan S. Sunge