PENERAPAN DATA MINING ANALISA PENYAKIT MENULAR PADA MANUSIA
Keywords:Data mining;Infectious disease;K-Means clustering;RapidMiner.
The use of data mining in technology is growing day by day. In this research, the author discusses the application of data mining in the medical field, data analysis of infectious diseases in humans and the use of infectious disease data in UPT Puskesmas Sei Langkai. Infectious diseases in humans are one type of disease that has a large amount of data and accumulates because basically infectious diseases have various causes and effects so that the purpose of this research was to identify and then analyze the highest to lowest levels of 7 types of data on infectious diseases in humans with a total of 1,212 patients in 2019 and 2020 using the K-Means clustering algorithm. From the data that has been processed get results that Acute Respiratory Infections and COVID-19 have the highest number of data in Tembesi, leprosy, dengue fever, and measles have the highest number of data in Sei Langkai, while HIV and TB has the highest number of data in Sei Pelunggut. The conclusion of this research is using the K-Means method and testing the RapidMiner application, it can facilitate data processing and has an accurate final value and effectively used in big data processing.