Penerapan Algoritma K-Means dan K-Medoid untuk Pengelompokkan Data Pasien Covid-19 - Gurning, Umairah Rizkya; Mustakim, Mustakim (Forum Kerjasama Pendidikan Tinggi, 2021)
At the end of 2019 in Wuhan, China, a new virus was discovered, that is Corona Virus Disease 2019. This virus causes serious health problems and has been declared as pandemic since March 11, 2020. It has caused death and claimed thousands of lives. This virus spreads very quickly and in various ways such as direct contact with patients, travelers, owners of congenital diseases and many other transmissions. To suppress the spread of this virus, the government has carried out various ways such as social distancing and screening or impromptu swabs in crowded centers. Due to the many types of transmission from this virus, this research was conducted to classify Covid-19 cases in Dumai City. It is hoped that the results of this study can be used as an illustration of the grouping of Covid-19 patient data by applying K-Means and K-Medoid as a grouping algorithm based on the type of transmission, age, gender, health services and district. Based on this research, the K-Means algorithm is more optimal than K-Medoid in classifying Covid-19 patient data, especially in Dumai City. It is proven that the best DBI K-Means value is 0.139 with k = 4