PENERAPAN ALGORITMA K-MEDOIDS TERHADAP DATA SISWA BERDASARKAN NILAI MATA PELAJARAN - Sri Darmayanti; Ahmad Faqih; Agus Bahtiar (JURNAL ILMIAH BETRIK, 2023)
The government has the responsibility of determining national education quality policies and standards and has a role to evaluate the implementation of education in the context of national education quality control. One way to evaluate the standards of primary and secondary education nationally is with the results of the National Examination (UN) achievements. To find out how the Application of the K-Medoids Algorithm to student data based on Subject Scores. In the Application of the K-Medoids Algorithm to student data based on Subject Scores, sample datasets or secondary data come from SMP Negeri 1 Tengah Tani Class IX which will be used as material for this study. For the Application of the K-Medoids Algorithm to student data based on Subject Scores, this uses the Cross Industry Standard Process for Data Mining (CRISP-DM) stage process consisting of Business Understanding, Data understanding, Data Preparation , Modelling, Evolution, Deployment. The results in this study obtained 3 clusters, namely cluster 0, cluster 1 and cluster 2 with the highest student scores, medium grades and lowest scores. The clustering obtained in this study k = 3 DBi value 0.234 The evaluation results of the K-Medoids algorithm resulted in cluster 3. Cluster 0 average BIND score 80.63, MTK mean 78.69, IPA mean 79.38 and BIG average 77.68. Cluster 1 average BIND score 86.92, MTK average score 82.04, IPA average score 82.13 and BIG average score 80.71. Cluster 2 average BIND score 86.24, MTK average score 81.74 , IPA average score 94.59 and BIG average score 81.06.