ANALISA CLUSTER DENGAN K-MEAN CLUSTERING UNTUK PENGELOMPOKAN DATA CYBERCRIME
DOI:
https://doi.org/10.51401/jinteks.v5i1.2209Keywords:
Analytics, Clusters, Cybercrime, K-MeanAbstract
The purpose of this research is to cluster or group cybercrime datasets. It is known that the potential for data-related crimes is very likely to occur. Several countries have long paid more attention to data security in cyberspace. In this study, the authors wanted to group or cluster data on Cybercrime data obtained from the Kaggle dataset. For this reason, it is necessary to classify types of cyber crime using the k-mean clustering method, which is a fairly simple clustering algorithm that partitions the database into several k clusters. Partition existing data into two or more groups. The results of this study are a grouping of data on cybercrime datasets which are divided into 3 groups or clusters which produce test results with K-Mean Clustering it is found that the K pattern used from 3 clusters has cluster 1 as the most dominant cluster with 20 data records
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