<![CDATA[This research describes the application of the K-means Clustering method to analyze e-learning user data. E-learning is a form of learning that uses electronic-based media. The main objective of this research is to cluster e-learning users based on the similarity of certain attributes and find patterns in the data. The research steps include collecting e-learning user data from keaglee website, from January 2004 to October 2021, cleaning the data to ensure accuracy and consistency, and applying clustering algorithm. This algorithm divides data into groups based on similarities. In this study, the data was divided into three groups using a value of k = 3. Through testing with the davies bouldin method, the best results were found in the 9th cluster with a centroid of 1,279. This cluster has similar e-learning user characteristics. K-means Clustering method successfully analyzes e-learning user data simply, efficiently, and easily interpreted. Grouping e-learning users based on similar attributes can be done using this method. This research can be the basis for further development in the use of clustering methods in e-learning.]]>
Copyrights © 2023
