Clustering the Students' Behavior on the e-Learning using the Density-Based Algorithm

The corona pandemic has changed the learning method from conventional to a more flexible one, such as through the internet. Consequently, students may have less direct interaction with teachers. This condition has made it difficult for teachers to monitor the students' behavior. This research works on this problem by focussing on the clustering of students' behavior using the DBSCAN, which is a density-based algorithm. Noises generated in this process can be considered students who do the uncommon behavior when taking the e-Learning system. Further, we evaluate the resulted clusters using the silhouette index to find their quality. The experimental result shows that the DBSCAN can differentiate clusters containing noises. By taking the silhouette index, the Manhattan distance parameter is superior to that of Euclidean.