Penerapan K-Means Clustering Untuk Pengelompokan Penyebaran Demam Berdarah Dengue (DBD) Di Kabupaten Deli Serdang
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Erica Melysa Sembiring
*
Universitas Budi Darma, Medan,
Indonesia
(*) Corresponding Author
Abstract
DBD is a disease that spread rapidly. Usually if there is an area affected by dengue fever, it is likely to spread to other people in the area. Due to the large number of DBD sufferers, so much data is collected and processing needs to be done on these data, such as the grouping of DBD sufferers data with the aim of focusing vector control in areas that are vulnerable to DBD. The area will be the main priority to carry out socialization related to the handling of DBD. Data mining is a series of processes to get useful information from large database warehouses. One function of the data mining process for finding groups or group identification is Clustering. There are two types of clustering methods, namely hierarchical clustering and non-hierarchical clustering, commonly called Kmeans. K-Means Clustering begins by determining the number of clusters first. The results obtained from grouping the data are in the form of areas that have the highest DBD potential in Deli Serdang. By using clustering method to do calculations, it can help solve problems in Deli Serdang Regency in classifying the data of DBD sufferers which is still done manually
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Pages: 673-677
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