Penerapan Data Mining Dalam Pengelompokan Kota Berdasarkan Provinsi Yang Tanggap Terhadap Ancaman Narkotika Dengan Menggunakan K-Medoids

Abstract

Narcotics is a problem that is not uncommon to be heard again by the public inside and outside the country. Especially at this time the development of Indonesia's population is very rapid so it is vulnerable to the threat of narcotics. There are several parties who take advantage of this development, especially the narcotics dealers, who are trying to destroy the younger generation by smuggling narcotics into the country and this is a potential market for illicit drug trafficking. The role of the community is very important in responding to this especially at the city / district level. To solve this problem the writer uses the K-Medoids method to classify cities that are responsive to the threat of narcotics. K-Medoids is one of the methods that exist in data mining by using clustering or grouping techniques. In this method the data that has been collected will be processed through the calculation process first by following the steps in the calculation process of the K-Medoids method that has been set so that it can get effective and accurate results. Using the K-Medoids method can find out the number of groupings in cities / districts that have high or low responsiveness. If a city has a low responsiveness to the threat of narcotics, it can help the City / District National Narcotics Agency (BNNK) to improve or add facilities such as UKS, socialize to the community and others in a City / Regency that has a low level of responsiveness to the threat narcotics, and increase public awareness of the dangers of narcotics. And if the city has a high response to the threat of narcotics, it is expected that the people in the city can maintain and increase their responsiveness (level of awareness) to the threat of narcotics. The results of grouping from the RapidMiner tools with the calsulation of k-medoids obtained a high clusters (C1) of 7 and a low cluster (C2)