Klasterisasi Pemetaan Kedisiplinan Pegawai Berdasarkan Rekap Kehadiran menggunakan Algoritma Clustering K-Means - Imam Ahmad Ashari; Purwono Purwono; Jatmiko Indriyanto; Arif Setia Sandi A. (METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi, 2025)
Employee discipline is one of the key success factors in a company. Work discipline has an important role in the formation of a positive work environment. One of the things that shows employee discipline is the time of attendance. Attendance time is usually recorded at the time the employee enters and leaves. Disciplinary information can be mapped into several groupings so that it is easy for decision makers to read. One of the computational methods that can perform data mapping is the K-Means Clustering method. The K-Means Clustering method can group data based on their characteristics. In this study, attendance data were analyzed using the K-Means method to obtain disciplinary groupings. The number of Clusters is calculated using the elbow method, 3 Clusters are obtained which are the best Cluster choices, namely Clusters 0, 1, and 2. The data analysis process shows Cluster 2 is the Cluster with the best level of discipline. From the analysis, it shows that the K-Means Clustering method can classify data based on employee discipline. Based on these results, decision makers can be helped in assessing employee discipline at Universita Harapan Bangsa using the disciplinary data grouping that has been made.