Descriptive Modeling Uses K-Means Clustering for Employee Presence Mapping

Warnia Nengsih, Muhammad Mahrus Zain
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Abstract

Human resource is valuable asset for an agency. The success of an institution is not only determined by the quality of its human resources, but also by the level of discipline. The discipline of an employee in an institution can be seen and measured by the level of attendance in doing a job, because the level of attendance is one of the factors that determine productivity. The current problem is the management level of the company that has difficulty in monitoring and controlling the employee attendance data. There needs to be a mapping and grouping to find out patterns of absence. Mapping or patterns that are obtained help management levels to monitor employees, take approaches and take action so as to improve employee discipline. In this study, it was used descriptive modeling with the implementation of the kmeans clustering method. The results of the mapping obtained help the management level in controlling and monitoring as a reference for the next policy maker.
描述性建模使用K-Means聚类进行员工状态映射
人力资源是机构的宝贵资产。一个机构的成功不仅取决于其人力资源的质量,还取决于其学科水平。在一个机构中,员工的纪律可以通过工作的出勤率来观察和衡量,因为出勤率是决定生产力的因素之一。目前的问题是公司管理层难以对员工考勤数据进行监控。需要进行映射和分组,以找出缺席的模式。获得的映射或模式有助于管理层监控员工,采取方法和采取行动,从而改善员工纪律。本研究采用描述性建模,实现kmeans聚类方法。所获得的映射结果有助于管理层进行控制和监测,为下一个决策者提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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