Clustering Pecandu Narkoba Menggunakan Algoritma K-Means Clustering

 (*)Ikhlasul Amal Mail (Universitas Islam Negeri Sumatra Utara, Medan, Indonesia)
 Raissa Amanda Putri (Universitas Islam Negeri Sumatra Utara, Medan, Indonesia)

(*) Corresponding Author

Submitted: November 19, 2023; Published: December 31, 2023

Abstract

With the rise of increasingly sophisticated technology in this time of globalization, it is exceptionally simple for the more extensive local area to manage exchanges (drugs). For this reason, the government is constantly trying to stop the spread of drugs among Indonesian people by using any media, ranging from verbal invitations, banners, posters, videos and photos displayed in schools, government and public places. The maltreatment of opiates and perilous medications (drugs) in Indonesia as of late has turned into a difficult issue and has arrived at a condition of concern with the goal that it has turned into a public issue.In order to make it easier for BNN to conduct monitoring and counseling to areas where there are many drug addicts, it is necessary to cluster data on drug addicts in Medan city. To solve the problem, it can be solved by clustering drug addicts in Medan city using the K-Means Clustering Algorithm. The data used comes from the BNN of North Sumatra Province, the data used is data on drug addicts in Medan City in 2020-2023. The purpose of clustering drug addict data in Medan city is to find out areas that are very high, high, low and very low levels of drug addicts.This study found that there are 2 sub-districts with the highest level of drug addiction, 7 sub-districts with a high level of addiction, 7 sub-districts with a low level of drug addiction, and 5 sub-districts with a lowest level of addiction.

Keywords


Drugs; Clustering; RapidMiner; Subdistrict; K-Means

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