Klasterisasi Gaya Belajar Mahasiswa Berbasis VARK dengan Algoritma DBSCAN untuk Personalisasi E-Learning - Maulana, Iqbal; Witanti, Wina; Melina (Institut Teknologi Garut, 2025)
The incompatibility between e-learning systems and students' learning styles remains a major challenge in improving the effectiveness of learning in Indonesian universities. This study aims to classify the learning styles of students at Jenderal Achmad Yani University using the VARK (Visual, Auditory, Read/Write, Kinesthetic) model, enriched with the Kano method. Data were collected from 1,000 students through the VARK-Kano questionnaire and analyzed using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. The clustering process was carried out by determining the optimal parameters using the k-distance plot, and the validity of the clusters was assessed using the Silhouette Score. The results showed that DBSCAN could form representative clusters of student learning styles and effectively detect data noise. This study contributed to the development of a cluster-based adaptive e-learning framework that could be implemented in Indonesian universities. These findings could serve as a basis for designing adaptive learning strategies that are more suited to student characteristics, thereby increasing the effectiveness of e-learning and learning motivation.