Komparasi Metode Decision Tree, Naive Bayes dan K-Nearest Neighbor pada Klasifikasi Kinerja Siswa

Komparasi Metode Decision Tree, Naive Bayes dan K-Nearest Neighbor pada Klasifikasi Kinerja Siswa

• By Tyas Setiyorini, Rizky Tri Asmono;
• Jurnal Nasional Terakreditasi
• 524
• 85-92

Dalam pendidikan, kinerja siswa merupakan bagian yang penting. Untuk mencapai kinerja siswa yang baik dan berkualitas dibutuhkan analisa atau evaluasi terhadap faktorfaktor yang mempengaruhi kinerja siswa.  Metode yang dilakukan masih menggunakan cara evaluasi 

berdasarkan hanya penilaian pendidik terhadap informasi kemajuan pembelajaran siswa. Cara tersebut tidak efektif karena informasi kemajuan pembelajaran siswa semacam itu tidak cukup untuk membentuk indikator dalam mengevaluasi kinerja siswa serta membantu para siswa dan pendidik untuk melakukan perbaikan dalam pembelajaran dan pengajaran. Penelitian-penelititan terdahulu telah dilakukan tetapi belum diketahui metode mana yang terbaik dalam mengklasifikasikan kinerja siswa. Pada penelitian ini dilakukan komparasi metode Decision Tree, Naive Bayes dan K-Nearest Neighbor dengan menggunakan dataset student performance. Dengan menggunakan metode Decision Tree didapatkan akurasi sebesar 78,85, dengan menggunakan metode Naive Bayes didapatkan akurasi sebesar 77,69 dan dengan menggunakan metode K-Nearest Neighbor didapatkan akurasi sebesar 79,31. Setelah dikomparasi hasil tersebut menunjukkan bahwa dengan menggunakan metode K-Nearest Neighbor didapatkan akurasi tertinggi. Hal tersebut menyimpulkan bahwa metode K-Nearest Neighbor memiliki kinerja yang lebih baik dibanding metode Decision Tree dan Naive Bayes.

• Katakunci Decision Tree, Naive Bayes, K-Nearest Neighbor, Kinerja Siswa
• URL http://ejournal.nusamandiri.ac.id/index.php/techno/article/view/16/11
• Penerbit ,

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