ANALISA PERBANDINGAN ALGORITMA K-MEANS, DECISION TREE, DAN NAE BAYES UNTUK SISTEM PENGELOMPOKKAN SISWA OTOMATIS
DOI:
https://doi.org/10.33197/jitter.vol2.iss2.2016.98Keywords:
Cooperative Learning, Automatic Grouping of Student, K-means, Decision Tree,Abstract
[INA]
Pembelajaran kooperatif adalah proses pembelajaran dengan mengikuti beberapa instruksi yang melibatkan siswa bekerja dalam tim untuk mencapai tujuan bersama dalam kondisi yang mencakup unsur-unsur yang telah ditentukan. Pembelajaran kooperatif mampu membuat siswa lebih percaya diri dan mampu mengungkapkan pendapat sehingga, dapat meningkatkan kemampuan siswa lebih efektif. Salah satu faktor pendukung pembelajaran kooperatif adalah komposisi siswa dalam kelompok. Sebaiknya, penentuan kelompok belajar di sekolah dilakukan berdasarkan pengamatan guru terhadap siswa. Namun hal ini tidak mudah karena membutuhkan waktu lama, dan membuat beban kerja guru bertambah. Sehingga, cara termudah penentuan kelompok belajar adalah ditentukan secara acak. Risikonya, proses pembelajaran kooperatif tidak berjalan dengan efektif. Dibutuhkan sebuah sistem yang mampu menentukan komposisi anggota belajar siswa secara otomatis. Dalam paper ini dipaparkan hasil analisis perbandingan algoritma K-means, Decision Tree, Naive Bayes terhadap data siswa yang dapat digunakan untuk pengelompokkan siswa. Dari hasil uji coba didapatkan Nae Bayes mampu mengelompokkan siswa lebih baik dengan nilai akurasi 70,37%.
Kata kunci :Pembelajaran Kooperatif, Pengelompokkan Siswa Otomatis, K-Means, Decision Tree, Nae Bayes.
[EN]
Cooperative learning is a process of learning that following some instructions involving students work in teams to achieve goal in a condition. Cooperative learning makes students more confident to tell opinions, and improve the ability of students more effectively. Formation of students in a group is important. Preferably, the determination of group learning in school is based on the observation of the student teachers. But, it is not easy because it takes a long time, and increase teacher task. The easiest way of determining groups of students by determined randomly. Hence, cooperative learning become inefficient. This process need a system that capable to determine the formation of the student's learning automatically. In this paper presented the results of a comparative analysis of K-means algorithm, Decision Tree, Nae Bayes. The best result of the experiment by Nae Bayes 70,37% accurately.
Keywords : Cooperative Learning, Automatic Grouping of Student, K-means, Decision Tree, Nae Bayes.
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