Penerapan Algoritma K-Medoids Data Mining untuk Clustering Wilayah Penderita Demam Berdarah Berdasarkan Data Set
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Diny Wahyuni
*
Universitas Gunadarma, Jawa Barat,
Indonesia
(*) Corresponding Author
Abstract
Disease is a disorder that occurs in the body, either in form or function, so that the body cannot work properly or normally. Dengue fever (DF) is an infection caused by the dengue virus that can cause accurate fever. Dengue fever is still a serious problem for public health. The Health Service in each region has the task of helping the community in dealing with dengue fever cases. Data sets are collections of data arranged in a structured format, such as tables or files, and contain information from various sources. In this study, data mining analysis was carried out using the Clustering technique using the K-Medoids method. The use of the K-Medoids Algorithm is said to be better at grouping datasets than k-means because K-Medoids is one of the effective clustering methods for dealing with small datasets. Data mining can be interpreted as the process of selection, exploration, and modeling of large amounts of data to find patterns or tendencies that are usually not realized. Clustering is a process of grouping records, observations, or grouping classes that have similar objects. The results obtained from the study show that the application of the K-Medoids algorithm can be done to form 2 clusters. In the first cluster there are 4 cluster results and in the second cluster there are 6 cluster results.
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