Implementation of K-Means Algorithm with Distance of Euclidean Proximity in Clustering Cases of Violence Against Women and Children

Abstract

Cases of violence that befell women and children in Tasikmalaya area are rampant, as well as low awareness of victims to follow up on their cases, it is necessary to recognize patterns in characteristics cases of violence that have ever existed. To obtain this pattern, data on cases violence was carried out on women and children using the clustering method, k-means algorithm, and using Euclidean proximity distance. The steps were taken following the CRIPS-DM model, starting from the process of understanding problem, understanding data, pre-processing data, modelling, evaluation, and pattern deployment. The results of this data extraction process are 4 clusters that have a DBI value of 0.657 and purity of 72%. To further facilitate the authorities cases of violence against women and children, an application was built using PHP programming language, MySQL as its database and software design methods using the waterfall model. With this application, it can be useful to facilitate the process of introducing further patterns of violence, so as to minimize emergence cases of violence against women and children in Tasikmalaya area.