Analisis Pengelompokan Peraturan Kementerian dengan Menggunakan K-Means Clustering - Resistania Anggita Putri; Nida Inayah Maghfirani; Galih Rendi Setyawan; Adam Achmad Rayhan; Nur Aini Rakhmawati (Jurnal Sisfokom (Sistem Informasi dan Komputer), 2020)
Thousands Ministry Regulations are found in Indonesia shows that it is a big number. These regulations are intended to focus on various fields in order to be upheld in the public interest. It has recently been discovered that the numbers are increasing and some are no longer enforced. Clustering in data mining can be used to find out the focus of problems are often discussed at each ministry. The method that will be used for clustering ministry regulation data is the K-Means algorithm. K-Means is a non-hierarchical data clustering method partitions data into clusters so data that has the same characteristics will be grouped into one cluster and data that has different characteristics will be grouped into another cluster. This research was conducted by conducting data collection, data cleaning, data processing, and visualization of the results. The results of this paper are grouping the best ministerial regulations into four clusters that have an inertia value of 405.142786991133. Cluster 0 is a collection of regulations on the empowerment of children, women, and victims of violence. Cluster 1 is a collection of regulations on environmental policies in both flora and fauna. Cluster 2 is a collection of regulations relating to science and professionalism. Cluster 3 is a collection of regulations relating to the safety of the creative economy in the field of tourism.