Pengelompokan Rumah Sakit di Jakarta Menggunakan Model DBSCAN, Gaussian Mixture, dan Hierarchical Clustering - Karli Eka Setiawan; Afdhal Kurniawan (Jurnal Informatika Terpadu, 2023)
It should be recognized that after the COVID-19 pandemic, the distribution of hospital facilities should be a primary concern in meeting society's fundamental rights to local and national governments. Therefore, it is necessary to analyze data that describes the current conditions regarding the distribution of hospital facilities, especially in Jakarta. Data analysis using a machine learning technique can provide several benefits, such as understanding the distribution of health workers, providing insight into human resource planning, monitoring hospital performance, and even becoming a reference for future targets by predicting the need for health workers. The research proposed three models of unsupervised learning, such as the density-based spatial clustering of applications with noise algorithm (DBSCAN), the Gaussian mixture model, and the agglomerative hierarchical model, to cluster a database of hospitals in Jakarta, Indonesia, that contains information on the number of medical workers in the hospital and its bed facilities. This data set was obtained from a web scraping process on the Ministry of Health of the Republic of Indonesia website in 2022. The comparison of the third unsupervised model showed that the Gaussian Mixture model produced the smallest Davies-Bouldin value with a value of 0.645713. This study is an extension of our previous investigation, in which the study is, as far as we know, the first to discuss the classification of the data on the list of hospitals in Jakarta based on information on the number of medical personnel and the number of bed facilities that make up the contribution of this research.