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All Journal Jurnal Ilmiah KOMPUTASI Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Journal of Computer System and Informatics (JoSYC) JIKI (Jurnal Ilmu Komputer dan Informatika) Media Akuntansi Perpajakan
Wahyuni, Diny
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Social Sciences

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Journal : Journal of Computer System and Informatics (JoSYC)

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Penerapan Algoritma K-Medoids Data Mining untuk Clustering Wilayah Penderita Demam Berdarah Berdasarkan Data Set Wahyuni, Diny
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5831

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.
Co-Authors Chatarina Umbul Wahyuni Fadhillah, Naufal Ibrahim, Lutfi Nabhan Muntu Abdullah, Muntu Nur Asni Ruhama, Syamsi Waskito, Bahtiar Winarko, Ananda Rizky Winda Widya Ariestya