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Dwi Arum Ningtyas
STMIK Nusa Mandiri
Computer Science & IT

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KLASIFIKASI SISWA SMK BERPOTENSI PUTUS SEKOLAH MENGGUNAKAN ALGORITMA DECISION TREE, SUPPORT VECTOR MACHINE DAN NAIVE BAYES Nurajijah Nurajijah; Dwi Arum Ningtyas; Mochamad Wahyudi
Jurnal Khatulistiwa Informatika Vol 7, No 2 (2019): Periode Desember 2019
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jki.v7i2.6839

Abstract

Dropping out of school in Vocational High School students is an educational problem that must be found out the causes, so that it does not happen again in the future. The purpose of this study is to classify student data so that it can be predicted that students who have the potential to drop out of school use the Decision Tree, Naive Bayes and Support Vector Machine algorithms. Then determine which algorithm is the best. The results showed that the Support Vector Machine algorithm was the best with an accuracy of 93.77% and Area Under the Curve of 0.990.
Co-Authors Mochamad Wahyudi Nurajijah Nurajijah