Studi Pengelompokan Multimetode Provinsi di Sumatera Utara Menggunakan Pendekatan PCA dan K-Means

Lubis, Fitra Hidayat (Universitas Islam Negeri Sumatera Utara)
Ashar, Suthan Farras (Universitas Islam Negeri Sumatera Utara)
M.S, OK Mhd Fahri Al-Faruqy (Universitas Islam Negeri Sumatera Utara)
Amari, Ahmad Boby (Universitas Islam Negeri Sumatera Utara)

ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA
Vol 9, No 2 (2025): November 2025

This study aims to classify regions in North Sumatra based on a set of social and economic indicators by applying a multi-method clustering approach. Principal Component Analysis (PCA) is employed to reduce data dimensionality and identify the most influential variables, while the K-Means algorithm is used to form clusters based on similarity of characteristics. The results indicate that the combination of PCA and K-Means can cluster provinces or regions more efficiently and interpretably. The resulting clusters reflect patterns of similarity among regions in terms of social and economic development, thus providing a basis for formulating more targeted regional development policies. These findings demonstrate that a multi-method approach can yield more comprehensive results in spatial data clustering.Keywords: Clustering, Principal Component Analysis (PCA), K-Means, multi-method, North Sumatra.