Klasterisasi Rumah Sakit berdasarkan Kunjungan Pasien menggunakan Algoritma K-Means: Data 2019-2023 - Mayola, Liga; Hafizh, M.; Syahputra, Hadi (Prodi Sistem Informasi Universitas Dharma Andalas, 2025)
Hospitals play a vital role in providing healthcare services to the community. Every day, patients visit hospitals to receive medical care. Over time, patient visit data continues to grow, resulting in a massive accumulation of data. This large volume of patient visit data can be clustered using data mining algorithms, providing strategic insights for resource management, facility planning, and improving the quality of healthcare services. The purpose of this study is to classify hospitals based on the number of patient visits over the past five years. The clustering process was conducted using the K-Means Clustering Algorithm. The research data was obtained from the Satu Data Sumbar website. Hospital patient visit data was grouped into three clusters. The results indicate that Cluster 1 (K1) represents hospitals with very high visit intensity, Cluster 2 (K2) represents hospitals with medium visit levels, and Cluster 3 (K3) represents hospitals with low visit levels. Keywords: