Open AccessPillar Algorithm in K-Means Method for Identification Health Human Resources Availability Profile in Central Java
Author(s) -
M. Nishom,
Sharfina Febbi Handayani,
Dairoh Dairoh
Publication year - 2021
Publication title -
juita : jurnal informatika/jurnal informatika
Language(s) - English
Resource type - Journals
eISSN - 2579-8901
pISSN - 2086-9398
DOI - 10.30595/juita.v9i2.9860
Subject(s) - human resources , pillar , java , cluster analysis , cluster (spacecraft) , computer science , centroid , identification (biology) , christian ministry , data mining , human health , algorithm , machine learning , medicine , artificial intelligence , engineering , environmental health , biology , operating system , philosophy , theology , management , structural engineering , economics , botany
Based on data from the Ministry of Health, the distribution ratio between health workers and patients in Indonesia is still not equal distributed. It influenced by the distribution of health human resources that are not in accordance with the ideal needs of health services. This results need to identify the profile of the availability of health human resources in Indonesia. In this study, an approach will be implemented to identify the profile of health human resources availability using K-Means Clustering with a combination of pillar algorithms in optimizing the selection of the initial cluster centroid. Chi-square analysis is used to determine the disparity in the needs of health human resources with the conditions of the availability of health human resources in the Central Java region. The data collection method used in this research is the observation method, while the scientific method used in this research is the K-Means Clustering method. The results showed that the application has been generated can dynamically determine the health human resource cluster based on the disparity category of health human resource availability in the Central Java region. In addition, the labeling of the Pillar K-Means cluster based on the Chi-square test has a high degree of accuracy, namely 80%.