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

Novia Gusmantoni | Zendy

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

Author(s) -

Novia Gusmantoni

Publication year - 2022

Publication title -

journal of computer scine and information technology

Language(s) - English

Resource type - Journals

ISSN - 2502-1486

DOI - 10.35134/jcsitech.v8i1.29

Subject(s) - cluster analysis , covid-19 , data set , computer science , cluster (spacecraft) , data mining , set (abstract data type) , social distance , geography , artificial intelligence , medicine , disease , pathology , infectious disease (medical specialty) , programming language

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.

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