Annisa Nadaa Shabrina, - and M. Afdal, -- and Siti Monalisa, -- (2023) Comparison Of K-Means, K-Medoids, and Fuzzy C-Means Algorithms for Clustering Drug User's Addiction Levels. Jurnal Sistem Cerdas, 6 (2). pp. 1-10. ISSN 2622-8254 (Submitted)

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Abstract

Abstract—Narcotics, psychotropics, and addictive substances are drugs that can activate brain systems, affect dopamine levels, and cause addiction. In Indonesia, a law requires drug addicts to receive treatment and care. To properly treat a drug addict, it is first necessary to determine the level of addiction. Data mining methods such as clustering can be used to assess a user's level of drug addiction. This study uses the clustering algorithms Fuzzy C-means, K-Medoids, and K-means. The performance of the three clustering algorithms will then be evaluated based on the average similarity of clusters. Data such as how many types of drugs that used, the length of time they were used, the psychiatric status, and the physical condition status are used. Clustering was accomplished using the data mining software RStudio. The clustering algorithms were then evaluated with the Davies Bouldin Index (DBI). Based on the analysis results, the K-Medoids algorithm was found to have the best average similarity value of cluster where the data from the grouping results can be used to determine the level of user addiction and, based on those levels, to suggest the best forms of treatment for users. Keywords—Drugs, Clustering, Fuzzy C-Means, K-Means, K-Medoids, DBI, RStudio.

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