K-MEANS APPLICATION IN CLUSTERING JUNIOR SCHOOLS BASED ON OF NATIONAL EXAM AVERAGE RESULTS IN SURABAYA CITY

Abstract

In each year each school will submit an individual report about its school to the Education Authorities, which also contains the lowest and highest national exam results, also the average score of all students who took the exam. Because there are so many junior high schools and the equivalent in the city of Surabaya, of course we will find it difficult to find which school gets good national exam scores from the four subjects tested. From the test, the resulting K-Means is one of the basic algorithms for forming clusters and can be applied to the Classification of Junior High Schools (SMP) and the equivalent based on the average national exam results. Clustering using the K-Means algorithm has a fairly good ability in the classification based on the average school exam results. Where in detection, and by input parameters such as threshold. Designing a new system of classifying schools can assist agencies in classifying schools based on average national exam scores. By using K mean clustering, we can classify into several groups based on the existing parameters.