CLUSTERING OF ALGORTIMA K-MEANS BASED NATIONAL EXAM SCORE DATA WITH ELBOW AND SILHOUETTE OPTIMIZATION - Rahman NST, Arief; Irmayani, Decy; Putra Juledi, Angga (INFOKUM, 2022)
The national examination is a system of evaluating basic education standards that supports student graduation. In accordance with the regulations of the Government of the Republic of Indonesia, the evaluation of learning outcomes aims to evaluate the achievements of national postgraduate students. The research methods carried out in this study start from problem analysis, data analysis, application design and data implementation. As the data obtained by the author, namely the National Vocational Examination Score Data for Vocational High Schools in Central Java Province for the class of 2019. But the data displayed is still random and uninformed. Then data mining techniques are needed to classify which schools are carried out using the k-means clustering method and using elbow and silhouette optimization, with the optimum k obtained K = 3 and K = 2 by calculation using the RStudio tool. It is expected to produce the best cluster for clustering. The overall average of K = 3 UN values Indonesian is 72.79906. The average score of UN English is 45,941. The average score of UN Mathematics is 41,324. Average UN Competency score 48.1947. The overall average of K = 2 UN values Indonesian 76.95. The average score of UN English is 33,425. The average score of UN Mathematics is 45.65. The average un competency score is 52.54. From the data on national test scores at the VOCATIONAL level, 3 groups were obtained using the k-means cluster with the elbow optimization method. On cluster 1 it has 707 members, the cluster has 152 members. Cluster 3 has 675 members