Analisis Cluster dengan Data Outlier Menggunakan Centroid Linkage dan K-Means Clustering untuk Pengelompokkan Indikator HIV/AIDS di Indonesia - Rini Silvi (Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya, 2018)
Cluster analysis is a method to group data (objects) or observations based on their similarities. Objects that become members of a group have similarities among them. Cluster analyses used in this research are K-means clustering and Centroid Linkage clustering. K-means clustering, which falls under non-hierarchical cluster analysis, is a simple and easy to implement method. On the other hand, Centroid Linkage clustering, which belongs to hierarchical cluster analysis, is useful in handling outliers by preventing them skewing the cluster analysis. To keep it simple, outliers are often removed even though outliers often contain important information. HIV/AIDS is a serious challenge for global public health since HIV/AIDS is an infectious disease attacking body’s immune system that in turn lowering the ability to fight infections which in the end causing death. HIV/AIDS indicators data in Indonesia contain outliers. This research uses gap statistic to define the number of clusters based on HIV/AIDS indicators that groups Indonesia provinces into 7 clusters. By comparing Sw/Sb ratio, Centroid Linkage clustering is more homogenous than K-means clustering. Using clustering, the government shall be able to create a better policy for fighting HIV/AIDS based on the dominant indicators in each cluster.