Implementation of k-Medoids Clustering Algorithm to Cluster Crime Patterns in Yogyakarta - Eduardus Hardika Sandy Atmaja (International Journal of Applied Sciences and Smart Technologies, 2019)
The increase in crime from day to day needs to be a concern for the police, as the party responsible for security in the community. Crime prevention effort must be done seriously with all knowledge that they have. To increase police performance of crime prevention effort, it is necessary to analyze crime data so that relevant information can be obtained.This study tried to analyze crime data to obtain relevant information using clustering in data mining.Clustering is a data mining method that can be used to extract valuable information by grouping data into groups that have similar characters.The data used in this study were crime patterns which were then grouped using K-medoids clustering algorithm.The obtained results in this study were three crime groups, namely high crime levelwith 4 members, medium crimelevel with 6 members and low crime level with 8 members.It is expected that this information can be used as material for consideration in crime prevention effort