K-Means Clustering Menghasilkan Tiga Segmen Layanan Sertifikasi Kapal Berbasis Karakteristik Operasional - Hermina; Handoyo Widi Nugroho (Jurnal Algoritma, 2026)
Uneven workload distribution in ship certification services is a major operational challenge for port authorities. This study applies K-Means Clustering to 18 types of certification services at KSOP Class I Panjang, involving 1,506 certificates issued from January to December 2025, using four operational variables: application frequency, completion time, number of documents, and service complexity. The Elbow Method identified K = 3 as the optimal number of clusters. Three groups were formed: Regular Services, consisting of 6 low-complexity services; Complex Services, consisting of 10 services with high technical requirements; and Dominant Services, consisting of 2 services that accounted for 62.4 percent of total certificate issuance. Cluster quality was confirmed by a Silhouette Score of 0.6221 and a Davies-Bouldin Index of 0.484. These findings contribute methodologically by demonstrating that service population-based segmentation produces valid clusters that can be directly applied as a basis for human resource reallocation, digitalization prioritization, and the formation of specialist teams within port authorities.