Penerapan Algoritma K- Medoids Dalam Mengelompokkan Tingkat Kasus Kejahatan di Setiap Provinsi
DOI:
https://doi.org/10.30865/klik.v2i3.298Keywords:
Drug Abuse; K-Medoids; Data; MiningAbstract
Drug abuse is a very serious problem and needs special attention from all parties. This is evidenced by the increasing number of drug cases and death cases due to the purchase of drugs issued from various media. The impact of drug addiction can be seen in one's physical, psychological and social. Drug abuse cases in each province in Indonesia have varying degrees of cases.This study aims to determine the cluster of drug abuse at high and low levels.The method needed for grouping drug case data is using data mining methods with the K-Medoids algorithm and using a computerized system that is rapidminer 5.3 application.The data used is sourced from the National Narcotics Agency of the Republic of Indonesia with the website: https://bnn.go.id/2015-2017 data which consists of 34 provinces to be divided into 2 clusters.From the calculation of the K-Medoids algorithm, high clusters were 10 provinces and low clusters were 24 provinces.This grouping is expected to be included for the government or related parties to further increase the socialization of the dangers of drugs in order to minimize mortality and crime due to drugs.
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Copyright (c) 2021 Nur Arief, Irfan Sudahri Damanik, Eka Irawan
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