Clustering Disease on Settlements Inhabitant In place seedy With Use Clustering Method


  • Ruine Buana Br Sitepu STMIK Kaputama
  • Achmad Fauzi STMIK KAPUTAMA
  • Rusmin Saragih STMIK KAPUTAMA



Clustering, K-Means, Data Mining, Disease, Settlements Slums


Residents living in slum areas often face serious problems related to public health, where the prevalence of disease tends to be high and its spread is difficult to control. The impact of the formation of slums for the community is that safety is threatened, health deteriorates, and social conditions worsen, causing many diseases for people living in slums. Therefore, this study aims to identify patterns and clusters of diseases that exist in residential areas in slums Binjai city using clustering method. The K-Means Algorithm clustering method was chosen because it is able to group data based on similar characteristics, so that it can help identify diseases in a more focused and efficient manner, using the MATLAB application is also very appropriate in this problem so that it can produce output from data mining that can be used in decision making. future decisions. By utilizing the data mining process using the clustering method, clustering can be a problem of grouping diseases in slum settlements. Based on the results of trials with 20 sample data conducted with MATLAB obtained in cluster 1 DHF cases with high slums, Cluster 2 cases of vomiting with moderate slums and cluster 3 cases of diarrhea with moderate slums. The results of this study are expected to provide in-depth insight into disease patterns and clusters in residential areas in slums.


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How to Cite

Sitepu, R. B. B., Achmad Fauzi, & Saragih, R. (2023). Clustering Disease on Settlements Inhabitant In place seedy With Use Clustering Method. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 3(1), 148–156.