Comparison Of Clustering Levels Of The Learning Burnout Of Students Using The Fuzzy C-Means And K-Means Methods

Learning burnout is an impact from work done in a manner Keep going continuously, causing fatigue physical and emotional. If boredom study no handled, got cause students productive inhibits potency student . So that this proposed method clustering for group level saturation students. The process research use Fuzzy C-Means K-Means. According to the previous study, K-Means can produce results best clusters. Destination of compare performance dataset used Testing was conducted with amount clusters 3,4,5. Test system get score Meanwhile, global silhouette coefficient 0.278 testing 0.287. Temporary Davies Bouldin Index, methods 0.224and value 0.384 value, generates more _ good However both them have weak structure because some data has distance between one far different clusters, so creates worth.