Penerapan Information Gain Dan Algoritma K-Means Untuk Klasterisasi Kedisiplinan Pegawai Menggunakan Rapidminer

One aspect of the discipline an employee in agency can be seen from side attendance. The level attendance is closely related to employee's disciplinary assessment. by looking at hours or check attendance, so that with these parameters you will get early, on time and late entry. This study explores data using k-means clustering algorithm. Before calculating algorithm, attribute selection information gain expected reduce attributes small weights. calculations are performed Rapidminer software. results showed had greatest influence was percentage entry a weight 0.783. Clustering algorithm produces three clusters performance value Davies Bouldin Index (DBI) -0.645. Cluster zero has fifteen members, cluster one thirty-six two fifty-two members. low discipline, high while moderate .