Perbandingan Algoritma K-Means dengan Fuzzy C-Means Untuk Clustering Tingkat Kedisiplinan Kinerja Karyawan
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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References
2. Agusta, Yudhi. K-Means – Penerapan, Permasalahan dan Metode Terkait. Jurnal Sistem dan Informatika. 2007; Vol. 3, 47-60.
3. Simbolon, Cary Lineker. Clustering Lulusan Mahasiswa Matematika Fmipa Untan Pontianak Menggunakan Algoritma Fuzzy C-Means. Buletin Ilmiah Mat. Stat. Dan Terapannya (Bimaster). 2013; Volume 02, No.1, 21-26.Referensi yang berasal dari situs web
4. Muzakir, Ari. Analisa Dan Pemanfaatan Algoritma K-Means Clustering pada Data Nilai Siswa Sebagi Penentuan Penerima Beasiswa. Prosiding Seminar Nasional Aplikasi Sains & Teknologi (SNAST) 2014; Pp 195-200.
5. Selviana, Nur Indah dan Mustakim. Analisis Perbandingan K-Means dan Fuzzy C-Means Untuk Pemetaan Motivasi Balajar Mahasiswa. Seminar Nasional Teknologi Informasi, Komunikasi dan Industri (SNTIKI) 8. 2016. Seminar Nasional Teknologi Informasi, Komunikasi dan Industri (SNTIKI) 9 ISSN (Printed) : 2579-7271 Fakultas Sains dan Teknologi, UIN Sultan Syarif Kasim Riau ISSN (Online) : 2579-5406 Pekanbaru, 18-19 Mei 2017 226
6. Mustakim. Pemetaan Digital dan Pengelompokan Lahan Hijau di Wilayah Provinsi Riau Berdasarkan Knoledge Discovery in Databases (KDD) dengan Teknik K-Means Mining. Seminar Nasional Teknologi Informasi, Komunikasi dan Industri (SNTIKI). 2012.
7. J. O. Ong, “Implementasi Algoritma K-Means Clustering Untuk Menentukan Strategi Marketing President University,” Ilmiah Teknik Industri, vol. 12, 2013.
8. Jiawei, Han, dkk Data mining Concepts and Techniques. USA: Elsevier Inc. All rights reserved. 2012.
9. Han, J dan Kamber, M., “Data Mining Concept and Technique”, Morgan Kaufmann, 2001.
10. Ghosh, S., Dubey, S.K., 2013. Comparative Analysis of K-Means and Fuzzy C-Means Algorithms. International Journal of Advanced Computer Science and Applications, Vol. 4, No.4
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