PENERAPAN DATA MINING UNTUK KLASIFIKASI PENGANGKATAN KARYAWAN MENGGUNAKAN ALGORITMA K-MEANS

Mudakir; Ahmad Turmudi Zy; Aswan S. Sunge

doi:10.51401/jinteks.v5i3.3369

Authors

Mudakir Universitas Pelita Bangsa
Ahmad Turmudi Zy Universitas Pelita Bangsa
Aswan S. Sunge Universitas Pelita Bangsa

DOI:

https://doi.org/10.51401/jinteks.v5i3.3369

Keywords:

employees, classification, data mining, clustering, k-means

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.

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