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Classification of Organizational Data Using the K-Means Algorithm

Yana Mulyana, Abdul Fadlil, Imam Riadi

Abstract


The University of Muhammadiyah Tasikmalaya (UMTAS) is a private university under the Muhammadiyah association located in Tasikmalaya City, West Java Province. As a form of developing student soft skills, UMTAS carries out many activities, one of which is through student organizations consisting of internal and external organizations. This research was conducted to find out which student organizations are categorized as excellent, good, and not good. The assessment attributes for grouping student organization data are the number of active members, activities in one year, organizational discipline, and achievements. Clustering uses the K-Means algorithm. The results obtained from calculations carried out manually and using the rapidminer application obtained the same results, namely clusters with the "excellent" category totaling 8 data (26.6%), clusters with the "good" category totaling 17 data (56.6%), and clusters with the "not good" category totaling 5 data (16.6%). The results of this study can be used by the head of the bureau of academic administration of student affairs and alumni in providing rewards in the form of priorities in organizational funding, awarding charters and punishments in the form of coaching and revoking student organization decrees.

Keywords


K-Means; rapidminer; clustering; organizations; classification.

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DOI: 10.33751/komputasi.v19i2.5676 Abstract views : 347 views : 298

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