Penerapan Data Mining Untuk Pengelompokan Siswa Berdasarkan Nilai Akademik dengan Algoritma K-Means
DOI:
https://doi.org/10.30865/klik.v3i3.627Keywords:
Datamining; K-means Algorithm; K-Means Clustering; Rapid MinerAbstract
The data mining process by applying the K-Means algorithm is carried out to group data into one or more groups, where data that has representative similarities is grouped into one group and data that has differences is included in another group. Grouping student data is done to facilitate schools in facilitating students based on differences in their ability to learn and participate in learning which consists of groups or classes of superior students, medium and low groups. The data application used for the calculation process is student data based on a centralized assessment in presenting reports on student learning outcomes using the results of report cards, namely the rapid miner. This assessment forms the basis of the attributes used in the calculation process to determine superior, medium and low class students. The purpose of this study is to manage centralized assessment data in presenting reports on student learning outcomes and grouping students in superior classes by implementing the K-means algorithm and conducting tests using the rapidminer application. So that student data can be managed and grouped effectively and efficiently
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