Perbandingan Metode K-Means dan Fuzzy C-Means Untuk Pengelompokan Pegawai Berdasarkan Nilai Kinerja dan Tingkat Kedisiplinan Pegawai
Keywords:
Penilaian Kinerja, MAPE, Clustering, K-means, Fuzzy C-meansAbstract
Bank Indonesia (BI) adalah bank sentral Republik Indonesia. Sebagai bank sentral, BI membutuhkan pegawai– pegawai dengan kompetensi terbaik dalam bidangnya masing– masing serta perilaku yang baik. Untuk mengetahui kualifikasi setiap pegawai dibutuhkan sebuah pengelompokan/clustering. Melalui proses penilaian kinerja serta tingkat kedisiplinan pegawai dilakukan proses pengelompokan agar dapat diketahui cluster–cluster yang terdapat didalamnya. Pembagian cluster pada penelitian ini mencirikan kualifikasi pegawai, dimana cluster 1 berisi pegawai dengan kualifikasi terbaik, cluster 2 berisi pegawai dengan kualifikasi baik, cluster 3 berisi pegawai dengan kualifikasi cukup, cluster 4 berisi pegawai dengan kualifikasi kurang dan cluster 5 berisi pegawai dengan kualifikasi Buruk. Rata-rata Persentase MAPE untuk keseluruhan cluster pada metode K-means lebih kecil dibandingkan dengan metode Fuzzy C-means. Persentase MAPE pada metode K-means sebesar 18,21% sementara pada metode Fuzzy C-means sebesar 21,07%. Rata-rata varian jarak antar anggota pada masing-masing cluster untuk keseluruhan cluster pada metode K-means lebih kecil dibandingkan dengan metode Fuzzy C-means. Rata-rata varian jarak pada metode K-means sebesar 0,350 sementara pada metode Fuzzy C-means sebesar 0,863. Rata-rata jarak antar cluster pada keseluruhan jarak antar tiap-tiap cluster pada metode K-means lebih besar dibandingkan dengan metode Fuzzy C-means. Rata-rata jarak antar cluster pada metode K-means sebesar 9,227 sementara pada metode Fuzzy C-means sebesar 6,465.References
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