Open AccessClustering Kualitas Kinerja Karyawan Pada Perusahaan Bahan Kimia Menggunakan Algoritma K-Means
Author(s) -
Sandra Regina,
Entin Sutinah,
Nani Agustina
Publication year - 2021
Publication title -
jurnal media informatika budidarma/jurnal media informatika budidarma
Language(s) - English
Resource type - Journals
eISSN - 2614-5278
pISSN - 2548-8368
DOI - 10.30865/mib.v5i2.2909
Subject(s) - cluster analysis , productivity , quality (philosophy) , attendance , cluster (spacecraft) , computer science , performance appraisal , business , mathematics , operations management , statistics , engineering , economics , management , philosophy , epistemology , macroeconomics , programming language , economic growth
Assessment of the quality of employee performance is one of the important things and is very much needed by the company, however, PT Clariant Adsorbents Indonesia does not currently have an employee performance quality system. This study aims to see the productivity of an employee and the effectiveness of an employee's performance in the future. Employee performance appraisal is divided into several clusters that are highly productive, moderately productive and less productive. The method used in this study is the K-means method, where the k-means method is the most popular method in the clustering algorithm. The k-means method looks for some of the most optimal partitions of the processed data by minimizing the error of the criteria using the optimal iteration. The variables used consist of employee names, work quality scores, responsibility values, cooperation values, attendance values, and discipline values. This research in processing data using Rapidminer Version 7.6.0.0.1 using the K-means method. The final result of this research is to get the grouping of the assessment into several categories that are very productive, quite productive and less productive and the clustering results are 0.42% for cluster 1, very productive category, which consists of 16 employee data, 0.47% for cluster 2 quite productive category, which consists of 18 employee data, 0.11% for cluster 3, less productive category, which consists of 4 employee data.