Application of K-Medoids Cluster Result with Particle Swarm Optimization (PSO) in Toddler Measles Immunization Cases

作     者：Eka Pandu Cynthia Indra Riyana Rahadjeng Erfan Karyadiputra Fauzi Yusa Rahman Agus Perdana Windarto Martalina Limbong Teguh Iman Hermanto E. Rusiadi Y Yarmani 

作者机构：Informatics Engineering Study Program Faculty of Science and Technology UIN Sultan Syarif Kasim Riau Indonesia Universitas Bina Sarana Informatika Jakarta Indonesia Universitas Islam Kalimantan Muhammad Arsyad Al Banjari Banjarmasin Indonesia STIKOM Tunas Bangsa Pematangsiantar Indonesia Akademi Keperawatan Surya Nusantara Pematangsiantar Indonesia Informatics Engineering Study Program Sekolah Tinggi Teknologi Wastukancana Indonesia Universitas Pembangunan Panca Budi Medan Indonesia Universitas Bengkulu Bengkulu Indonesia 

出 版 物：《Journal of Physics: Conference Series》 

年 卷 期：2021年第1933卷第1期

学科分类：07[理学] 0702[理学-物理学] 

摘      要：The objective of the research was to analyze the clustering method by optimizing Particle Swarm Optimization (PSO) in the case of measles immunization for children under the age of 5. The research data source used is the North Sumatra Province Central Bureau of Statistics (https://***/). The data used in 2019 included 33 records with variables BCG, DPT-HB3/DPT-HB/Hib3, CAMPAK (MEASLES + RUBELLA), POLIO 4 and HEPATITIS B. The methods used were k-medoids and PSOs. This method is used to determine the value of the Davies Bouldin Index (DBI) before the cluster value is determined (k). The resulting k values were compared to k-medoids without PSO. The best results of k-medoid and PSO will be tested by classification to see the accuracy value of the cluster formed. The results showed that the optimization of k-medoids and PSO was better with the number of clusters (k = 5) than 0.078. (DBI). The results also show that the accuracy of the cluster formed is 95% with a correlation of 0.98.