Data Clustering Menggunakan Metodologi CRISP-DM Untuk Pengenalan Pola Proporsi Pelaksanaan Tridharma

*Irwan Budiman -  Fakultas MIPA, Universitas Lambung Mangkurat Banjarbaru, Kalimantan Selatan, Indonesia
Toni Prahasto -  Magister Sistem Informasi, Program Pascasarjana Universitas Diponegoro, Semarang, Indonesia, Indonesia
Yuli Christyono -  Magister Sistem Informasi, Program Pascasarjana Universitas Diponegoro, Semarang, Indonesia, Indonesia
Received: 20 Jan 2014; Published: 21 Jan 2014.
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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 theEPT-PT)  aims  to  ensure  the  implementation  of  the  faculty  task  runs  according  to  the  criteria  set  out  in  legislation.  Data  clusteringTridharma  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

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