KLASIFIKASI SISWA SMK BERPOTENSI PUTUS SEKOLAH MENGGUNAKAN ALGORITMA DECISION TREE, SUPPORT VECTOR MACHINE DAN NAIVE BAYES

Nurajijah Nurajijah (STMIK Nusa Mandiri)
Dwi Arum Ningtyas (STMIK Nusa Mandiri)
Mochamad Wahyudi (Universitas Bina Sarana Informatika)

Jurnal Khatulistiwa Informatika
Vol 7, No 2 (2019): Periode Desember 2019

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.