IMPLEMENTATION OF EMPLOYEE DISCIPLINE CLUSTERING AT GOTTING SIDODADI VILLAGE OFFICE BANDAR PASIR MANDOGE USING K-MEANS ALGORITHM
Abstract
Discipline is the key to the success of an organization in achieving its goals, because discipline is an operative function of human resource management which is very important and will create quality employees. Employee discipline can be seen through employee attendance. The importance of evaluating employee discipline levels to improve services to the community and make it easier for leaders to find out the level of discipline of Gotting Sidodadi Village office employees, Bandar Pasir Mandoge currently has not grouped the level of employee discipline because there is no system that can assist in the process. The grouping of employees' discipline levels is used the K-Means Clustering method in this study. The application of the K-means Clustering method is implemented in an application made with 3 clusters of 14 data samples, and not absent from home. The data used is attendance data for 14 employees from 2018 to 2021. The results of this study are that the k-means algorithm is able to classify the data with the highest level of discipline. medium and low with the existence of this application system, it is hoped that the leadership can easily find out the level of discipline of employees at the Gotting Sidodadi Village Office so that they can provide bonuses for employees with high levels of discipline, and sanctions for employees with low levels of discipline.
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