CLUSTERING DATA NILAI ADAPTIF SISWA MENGGUNAKAN ALGORITMA K-MEANS

Student success rates and low student failure rates are a reflection of the quality of education, at this time the value does not determine the success of students in the next stage, but the uniqueness in itself that is represented in each grade they achieve, maybe students fail in mathematics but he succeeded in chemistry. it does not indicate he failed but he has shown its strengths in other respects namely other subjects, therefore this study seeks to find the positive side of students by classifying the value of subjects achieved by implementing the K-Means method in its application which will provide cluster output of each subject. K-Means method was chosen because this method can group items in k groups (where k is the number of groups or clusters desired, so the results of this termination are clusters of student grades grouped by subjects. K-Means is effective for clustering data by showing good accuracy value, this is indicated by the results of evaluations using Bouldin index davies on student data using K-Means which is equal to -1,478. Keywords: Cluster, K-Means, Education, Davies-Bouldin.