使用K-Means算法的组织数据分类

Yana Mulyana, A. Fadlil, I. Riadi
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摘要

塔西马来亚穆罕默德大学(UMTAS)是一所私立大学,位于西爪哇省塔西马来亚市的穆罕默德协会。作为培养学生软技能的一种形式,UMTAS开展了许多活动,其中之一是通过由内部和外部组织组成的学生组织。此次调查的目的是找出优秀、优秀、不优秀的学生团体。分组学生组织数据的评估属性是活跃成员的数量、一年中的活动、组织纪律和成就。聚类使用K-Means算法。人工计算和使用rapidminer应用程序计算得到的结果相同,即“优秀”类别的聚类共有8个数据(26.6%),“良好”类别的聚类共有17个数据(56.6%),“不太好”类别的聚类共有5个数据(16.6%)。本研究结果可为校友局教务处领导在组织经费优先奖励、颁发特许、教练处分、撤销学生组织法令等方面提供参考。
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Classification of Organizational Data Using the K-Means Algorithm
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.
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