ALGORITMA PENGELOMPOKAN MENGGUNAKAN SELF-ORGANIZING MAP DAN K-MEANS PADA DATA SUMBER DAYA MANUSIA PROVINSI INDONESIA

Abstract

Unsupervised data is a data type that will be encountered a lot in real-world problems. Example of unsupervised data is data about the condition of a region based
on geographic or demographic information. Unsupervised methods that have beentested and studied are clustering methods.
The combination of the clustering algorithm has been well studied. Thecombined algorithm that has been implemented is Self Organizing Map (SOM) and KMeans algorithm where K-Means algorithm is used to clarify the visualization result of SOM algorithm. That combined algorithm is implemented on a dataset of human
resource information from 33 provinces in Indonesia with evaluation of clustering experiments using the silhouette algorithm. From the experiment results, it can be seen
that the provinces in Indonesia can be grouped based on information owned human resources.