Penentuan Klustering Indeks Pembangunan Manusia Provinsi Jawa Tengah dengan Metode K-Means Berbasis Web - Aryasatya, Rakryan; Lusiana, Veronica (Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), 2024)
The Human Development Index uses a data clustering algorithm, namely the K-Means algorithm, which is the simplest clustering algorithm compared to other algorithms. This algorithm is one of the most important algorithms in data mining. K-Means divides the data and then groups it into several similar clusters and separates each cluster based on the differences between each cluster. The aim of this research is to design and implement the Human Development Index for Central Java Province using a web-based k-means clustering algorithm. This research is a qualitative research in the field of electrical engineering, especially in the field of software. This research was conducted by analyzing data using the K-Means Clustering Algorithm for the Human Development Index. The implementation of the k-means clustering algorithm into the clustering system provides effective data grouping classification results and the process of each centroid distance rotation literacy, the determination of cluster points is formed, human data as a reference object saves more time when clustering the Human Development Index. The application of this clustering results in more flexible information that can be accessed at any time by users who are given access rights to utilize the data. The application of the K-Means Clustering Algorithm to obtain the results of the Human Development Index requires an information system implementation to form four clusters