Analysis Of Employee Discipline Based On Digital Attendance With The K-Means Algorithm Method - Yulya Muharmi; Sri Nadriati (JURNAL TEKNOLOGI DAN OPEN SOURCE, 2022)
Employee discipline is one of the most important factors for the progress of the company. PT. Sumatra Core Cellular (PT. SIS) Pekanbaru has implemented a digital attendance application, but the company has not evaluated the application to determine the level of employee discipline. Data mining is the process of extracting useful information from a large database population. One of the data mining methods is the K-Means algorithm. The data mining process uses the method of K-Means algorithm with 2 clusters namely discipline and less disciplined categories. The data used is attendance data of 159 employees, namely data on tardiness, non-attendance (TAP), attendance hours and 4 selected questionnaire questions. Tools for grouping with the Rapidminer application. Using the K-Means algorithm method, it is known that cluster 0 consists of 133 employees or 83.64% with a disciplined category and cluster 1 produces 26 employees or 16.35% with a less disciplined category. Judging from the accuracy of attendance hours, employees in cluster 0 are more likely to be present at 07.45 - 08.15 and in cluster 1 they are more likely to be present at 08.15 - 08.30. In terms of lateness and TAP, there is a lack of discipline in cluster 1. From the level of satisfaction with the application based on 4 selected questions, it can be concluded that the digital attendance application increases the discipline of the employees. The results of this analysis can be used as a reference for evaluating employee discipline, determining promotions and improving employee discipline in the future.