Application of Data Mining Clustering the Development of Covid-19 Using K-Medoids Method

Novia Gusmantoni

doi:10.35134/jcsitech.v8i1.29

Abstract

At the beginning of March, Indonesia was hit by the entry of the corona virus (covid) outbreak. Every day the cases of the spread of covid-19 in Indonesia continue to increase. The public is asked to carry out social distancing in order to break the chain of the spread of Covid-19 which is spread in various regions in Indonesia. Therefore, the data that has been accommodated is certainly a lot, from the data it can be seen that the patterns of determining the grouping of the spread of Covid-19 are based on test scores. Public. K-Medoids is a partitional clustering analytical method that aims to get a set of k-clusters among the data that is closest to an object in grouping a data. The results of the study of grouping the spread of the new covid-19 show that people come from various regions in Indonesia. Characteristics with a body temperature above 36.9 C and accompanied by fever and continuous cough show one of the symptoms of Covid-19.

Highlights

Introduction zoning for each province in Indonesia, while we all Corona (Covid-19) is a family of viruses that infect need data changes every day
Covid-19 is an analysis to obtain clear relationships and conclusions infectious disease characterized by the presence of about previously unknown relationships so that they symptoms in the acute respiratory tract coronavirus 2 can be reached [5][6]
Data at the West Sumatra Provincial Health Office is in the form of numbers, where the number is a value for the positive COVID-19 variable, recovered from COVID-19, and died from COVID-19 can be seen in Table. 1

Summary

Research Method stage until there is no medoid change so that clusters

This study uses research stages starting from and their respective cluster members are obtained. The authors took data from the West Sumatra Provincial Health Office. Data at the West Sumatra Provincial Health Office is in the form of numbers, where the number is a value for the positive COVID-19 variable, recovered from COVID-19, and died from COVID-19 can be seen in Table. The analysis was 8 carried out on the data obtained by using the K- 9 Medoids algorithm. The data will later be processed for analysis using the randomly select one of the data in each cluster as a clustering method with the following stages: candidate for a new medoid. 2. cluster to get a new group of k objects as medoid. Journal of Computer Science and Information Technology Volume 8 Issue 1 (2022) 22-26 23

Data Normalization

Provinces included in Cluster 1

Provinces included in Cluster 3

Conclusion