Perbandingan K-Means dengan Hierarchical Clustering Untuk Pengelompokkan Tingkat Pengangguran di Sumatera Utara

Enzelina Feronika Situmorang
Yunita Angelina Sihombing (Universitas Methodist Indonesia)
Eva Lina Damayanti Samosir (Universitas Methodist Indonesia)
Indra M. Sarkis S. (Universitas Methodist Indonesia)

TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Vol 4 No 2(SEMNASTIK) (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akunt

Unemployment is a crucial issue faced by many countries, including Indonesia. This study compares two data clustering methods, K-Means and Hierarchical Clustering, to group districts/cities in North Sumatra based on the Open Unemployment Rate (OUR). The K-Means method is known for its speed and simplicity in partitioning data into clusters by determining centroids as the central points, while Hierarchical Clustering organizes data into a more complex hierarchy without requiring a predefined number of clusters. The OUR dataset used in this study was obtained from various districts/cities in North Sumatra and processed using statistical software to apply both methods. The results indicate that the K-Means method provides superior clustering quality with a Silhouette Score of 56.50%, compared to Hierarchical Clustering, which obtained a score of 43.69%. These findings suggest that the K-Means method is more effective in identifying unemployment patterns in the region. This insight can serve as a reference for policymakers in formulating more targeted strategies to address unemployment in North Sumatera.