Clustering of Study Program Using of Block-Based K-Medoids
Abstract
The purpose of this research is to classify Study Programs based on eleven mixed data from Internal
Quality Management System (QMS) indicators. This grouping can provide a clearer picture of how
QMS affects the performance and quality of study programs. By understanding these clusters, universities can identify and design more effective strategies to improve the quality of education. The data
used comes from the National Accreditation Board for Higher Education (BAN-PT) and the website
database, which consists of seven numerical variables: number of lecturers, percentage of doctors, percentage of professors and associate professors, student enumeration, percentage of graduates, program
experience, and availability of laboratories. Meanwhile, the categorical variable consists of four variables: National Accreditation Board of Higher Education (BAN-PT) research ranking, accreditation,
international recognition, and level of community service. The clustering method used is the blockbased k-medoids (block-based KM), and multivariate analysis of variance (MANOVA). We applied the
Deviation Ratio Index based on K-Medoids (DRIM) to determine the number of clusters. This research
results that the optimal number of groups that must be formed is three. Based on MANOVA the results
showed that the group consisting of 12 study programs had better QMS outcomes than the other two
groups.
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