Silhouette Density Canopy K-Means for Mapping the Quality of Education Based on the Results of the 2019 National Exam in Banyumas Regency

Abstract

Mapping the quality of education units is needed by stakeholders in education. To do this, clustering is considered as one of the methods that can be applied. K-means is a popular algorithm in the clustering method. In its process, K-means requires initial centroids randomly. Some scientists have proposed algorithms to determine the number of initial centroids and their location, one of which is density canopy (DC) algorithm. In the process, DC forms centroids based on the number of neighbors. This study proposes additional Silhouette criteria for DC algorithm. The development of DC is called Silhouette Density Canopy (SDC). SDC K-means (SDCKM) is applied to map the quality of education units and is compared with DC K-means (DCKM) and K-means (KM). The data used in this study originated from the 2019 senior high school national examination dataset of natural science, social science, and language programs in the Banyumas Regency. The results of the study revealed that clustering through SDKCM was better than DCKM and KM, but it took more time in the process. Mapping the quality of education with SDKCM formed three clusters for social science and natural science datasets and two clusters for language program dataset. Schools included in cluster 2 had a better quality of education compared to other schools.