Clustering Student's Satisfaction in Complex Adaptive Blended Learning with the Six Value System Using the K-Means Algorithm

Complex adaptive blended learning with the six value system is expected to improve the quality of learning. It is also expected to improve high order thinking skills. But the level of student’s satisfaction in Complex adaptive blended learning with the six value system varies in results. This study aims to determine the grouping of student’s satisfaction in complex adaptive blended learning with the six value system. The object of the study was conducted in 3 (three) vocational high schools (SMK) in Cirebon City, West Java Province, Indonesia for digital simulation subjects with a purposive sample of 150 students. Data about student’s satisfaction is grouped using the K-Means algorithm with the optimization of generation method. Several stages are carried out in grouping student’s satisfaction, starting with randomly determining initial centroid values. The K-Means algorithm process ends if there is no change in centroid value between one iteration and another iteration. Furthermore, performance measurements are performed using the Cluster Distance Performance method. The results are obtained by the performance Vector with parameters K = 4. The average distance in the centroid is -0.107, the average distance in the center of cluster 0 is -0.109, the average distance in the center of cluster 1 is -0.109, the average distance in the center of cluster 2 of -0,100, and the average distance in the center of cluster 3 is -0,106, with Davies Bouldin index of -1,049.