VISUALISASI DATA PENGELOMPOKKAN KELULUSAN MAHASISWA DENGAN ALGORITMA CLUSTERING K-MEANS

Abstract

The implementation of technology in universities is rapidly increasing, Either of them are creating various groupings such as financial, library, and academic data. This research implemented the graduation data Udayana University Faculty of Engineering students by applying the data mining method, specifically the K-Means Clustering algorithm. The study aimed to assist Udayana University Faculty of Engineering in comprehending detailed graduation patterns by categorizing the data based on variables including students' origin (district or city) and the study programs in engineering faculty. This research also focused on the visualization design of clustered data in the form of charts and graphs. This data visualization dashboard was created using Google Looker Studio, which the clustered student graduation data is visualized in the form of charts and clustering graphs, which can make it easier for Udayana University Faculty of Engineering to achieve new information and useful insights to improve the academic performance of Udayana University Faculty of Engineering itself.