Penerapan Data Mining Untuk Penjurusan Kelas dengan Menggunakan Algoritma K-Medoids
- Jhiro Faran Universitas Nasional, Jakarta, Indonesia
-
Rima Tamara Aldisa
*
Universitas Nasional, Jakarta,
Indonesia
(*) Corresponding Author
Abstract
Class assignments are carried out to focus students on the subjects that will be studied during Senior High School (SMA). Class majors are generally carried out in class of all the main values used in the class majoring process. This is a problem with the class majoring process, where mistakes often occur in the class majoring process. Mistakes regarding class majors made by students will have quite a fatal impact on the student, apart from not being able to change classes, it will also have a laziness effect on the student because it does not match the student's abilities. Solving this problem can be done using a technique called data mining. The solution to this problem is done using clustering. The K-Medoids algorithm is the algorithm used to solve the problems in this research. The process of grouping or forming clusters in the K-Medoids algorithm is based on calculating the closest distance to each object, calculating the closest distance is based on determining the centeroid value first. The K-Medoids algorithm can form 2 (two) clusters according to existing class majors. The results obtained show that there are 3 (three) alternatives included in cluster 1 and also 12 (twelve) alternatives included in cluster 2.
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