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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

Perbandingan Algoritma K-Means dengan Fuzzy C-Means Untuk Clustering Tingkat Kedisiplinan Kinerja Karyawan Nova Agustina; Prihandoko Prihandoko
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 2 No 3 (2018): Desember 2018
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (635.776 KB) | DOI: 10.29207/resti.v2i3.492

Abstract

STT Bandung is university that has great potential to become a leading university of Bandung. To achieve the purpose of college, one of the stages that must be done is the evaluation of employee performance, namely by monitoring employee discipline. To facilitate the determination of the level of employee discipline is required data mining techniques to cluster of data. In data mining there are several methods of data clusters, which is often used is the method of K-Means with Fuzzy C-Means. Based on the research conducted both methods are grouping employee performance data into 3 clusters, namely high performance level, the level of medium performance level and low performance level. The results of this study indicate that the Fuzzy C-Means method is a better method than K-Means to do data clustering on the level of employee performance in STT Bandung because the value of validation is close to 1. Keywords: Data Mining, Comparison, K-Means, Fuzzy C-Means