DATA CLUSTER MAPPING OF GLOBAL COVID-19 PANDEMIC BASED ON GEO-LOCATION: DATA CLUSTER MAPPING OF GLOBAL COVID-19 PANDEMIC BASED ON GEO-LOCATION - Iskandar Fitri; Muchamad Refly Asmar; Albar Rabhasy (Institute of Computer Science (IOCS), 2020)
The spread of the covid-19 virus pandemic is very fast, where was start of the virus spreading from Wuhan City, Hubei Province, China and suddenly spread out widely to almost around the word. According that kind of pandemic phenomena, this research was conducted to make clustering based on data of latitude and longitude due global spreading of Covid-19 use DBSCAN (Density-Based Spatial Clustering Of Applications With Noise) and K-Means to find a level accurate and suitable in calculating for this pandemic case as the alternative choices for condition analyse in decision making purpose. The algorithm had developed calculate based on characterization from geolocation of the country which is to determine the number quality of cluster use Silhoutte Coefficient and Elbow Methods. Therefore, from calculated results can be analyse similarity of covid-19 spreading pattern refer to clustering in each province or country. From data testing show that DBSCAN method separate the data of noise points with eps=22 and minimum pts=4, and for K-Means method with k = 3. After calculation by use the two methods, finally, can visualize the mapping cluster continent of Asia, Europe and Africa with showing the pattern of increasing covid19 cases that can began controlled. The other result show cluster for continent of north and South America have increased significant and the Australian Continent cluster gets the lowest case and can controlled.