Penerapan Data Mining Untuk Pengelompokan Terhadap Kualitas Kinerja Karyawan Dengan Menggunakan Algoritma K-Medoids Clustering

 Abdul Karim (Universitas Budi Darma, Medan, Indonesia)
 (*)Shinta Esabella Mail (Universitas Teknologi Sumbawa, Sumbawa, Indonesia)
 Kusmanto Kusmanto (Universitas Al Washliyah Labuhanbatu, Rantoprapat, Indonesia)
 Muhammad Hidayatullah (Universitas Teknologi Sumbawa, Sumbawa, Indonesia)
 Sudi Suryadi (Universitas Al Washliyah Labuhanbatu, Rantoprapat, Indonesia)

(*) Corresponding Author

Submitted: January 30, 2024; Published: April 30, 2024

Abstract

HR management is recognized as a global issue and an integral part of competitiveness in the arena of globalization. The organizational structure is the placement of tasks from the very top to the placement of very basic tasks. Assessing the quality of employee performance is one of the work evaluation sessions that can provide the best for industry and citizens. In position placement, if someone does not suit the position they have, it will cause problems such as the company's operational processes not running well. Performance appraisal of employees aims to see the performance results that have been carried out or given by employees when occupying a position. Problems related to performance appraisal are important problems that must be resolved immediately. Data mining is a data processing process in the past, where data in data mining is a collection of data that has been collected over a certain period of time. Information data mining is a series of processes for exploring added value in the form of data produced by extracting and identifying patterns in an information base. Clustering is a part of data mining that aims to group based on the formation of new clusters. The K-Medoids algorithm is a partitional clustering procedure that minimizes the distance between labeled points. The K-Medoids algorithm is a classic Clustering partition technique that groups data sets of ni objects into k groups known a priori. From the results of research conducted using the K-Medoids method, 3 clusters were obtained. Where in cluster 1 there are 4 employees, in cluster 2 there are 3 employees and in cluster 3 there are 3 employees.

Keywords


Assessment; Performance; Employee; Data Mining; K-Medoids Algorithm

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