<![CDATA[Human resources are a crucial component in an institution, particularly in hospital environments. This study aims to analyze employee attendance data at RSUP Dr. Rivai Abdullah and to develop a model for grouping attendance levels using the K-Means clustering method. The K-Means method is applied to classify attendance data based on several variables, including discipline, punctuality, and lateness. The data used in this study consist of employee attendance records over a specific period obtained from the hospital’s personnel information system. To evaluate the quality of the clustering results, the Davies-Bouldin Index(DBI) was employed. The DBI value is used to determine the optimality of the generated clusters, where a smaller DBI value indicates better clustering quality. The results show that employee attendance data can be grouped into several clusters representing levels of discipline, namely disciplined, punctual, and late employees, with a DBI value indicating optimal clustering performance. These clustering results can assist hospital management in identifying groups of employees with low attendance levels who require further attention and evaluation. Therefore, the implementation of the K-Means clustering method evaluated using the Davies-Bouldin Index proves to be effective in supporting objective and measurable analysis of employee attendance data and can serve as a basis for decision-making related to performance evaluation and employee discipline policies.]]>
