DATA CLUSTERING MENGGUNAKAN METODOLOGI CRISP-DM UNTUK PENGENALAN POLA PROPORSI PELAKSANAAN TRIDHARMA

Abstract

Quality of human resources faculty can be reflected from the implementation of productivity and quality Tridharma (education, research, community service and supporting field activities). Lecturer Workload and Evaluation of Higher Education Tridharma (BKD and the EPT-PT) aims to ensure the implementation of the faculty task runs according to the criteria set out in legislation. Data clustering Tridharma implementation is needed to get some knowledge of the pattern of Tridharma implementation at college. Clustering as a data mining technique should be scalable, reliable and meet an agreed standard. CRISP-DM is the standardization of data mining is used in this study. The results of data clustering found the pattern of proportion of Tridharma into 3 clusters representing patterns: professionals, managers and teachers. Keywords: Clustering, CRISP-DM, K-Means, Tridharma