Perbandingan Algoritma K-Means dengan Fuzzy C-Means Untuk Clustering Tingkat Kedisiplinan Kinerja Karyawan

  • Nova Agustina Sekolah Tinggi Teknologi Bandung
  • Prihandoko Prihandoko Universitas Gunadarma
Keywords: Data Mining, Comparison, K-Means, Fuzzy C-Means

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

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Published
2018-12-11
Section
Technology Information Article