Clustering Analysis of Civil Servant Data in Binjai City Based on Regional Work Unit and Age Using the K-Means Clustering Method

Abstract

Civil Servants (Aparatur Sipil Negara/ASN) play a strategic role in government administration and public services in Binjai City. However, the management of ASN data has thus far remained largely administrative and descriptive, failing to adequately reveal employee distribution patterns based on regional work unit, age, and rank. This study aims to cluster ASN data in Binjai City using the K-Means Clustering algorithm based on the variables of regional work unit, age, and rank. The dataset consists of 498 civil servants obtained from the Personnel and Human Resource Development Agency (BKPSDM) of Binjai City, after removing anomalous data belonging to Rank I/d. The clustering process was carried out using MATLAB through the stages of data selection, transformation into numerical form, normalization, Euclidean Distance calculation, and iteration until the centroids stabilized. Testing was conducted on three cluster scenarios, namely k=3, k=4, and k=5, evaluated using Sum of Squared Error (SSE), the Davies-Bouldin Index, the Silhouette Score, and Variance analysis. The results show that k=4 produced the best clustering outcome, with a Silhouette Score of 0.5705, a Davies-Bouldin Index of 0.9309, an SSE of 29.9369, and a more balanced Variance distribution among clusters. The clustering results yielded four distinct groups of civil servants, one of which was identified as consisting predominantly of senior employees from the Education Office, requiring particular attention in succession planning. The findings of this study are expected to serve as a basis for policy-making in the management of human resources within the Binjai City Government.