Details

Autor(en) / Beteiligte

• Harwati
• Virdyanawaty, Riezky Ikha
• Mansur, Agus

Titel

Drop out Estimation Students based on the Study Period: Comparisonbetween Naïve Bayes and Support Vector Machines Algorithm Methods

Ist Teil von

• IOP conference series. Materials Science and Engineering, 2016-01, Vol.105 (1), p.12039-12046

Ort / Verlag

Bristol: IOP Publishing

Erscheinungsjahr

2016

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Industrial Engineering is one of the departments in Faculty of Industrial Technology. It has more than 200 reshmen in every academic year. However, many students are dropped out because they couldn't complete their study in appropriate time. Variables that influence the drop out case are not yet studied. The objective of this paper is discovering the highest accuracy level between the two methods used, i.e. Naïve Bayesand Support Vector Machines algorithms. The method with the highest accuracy will be discovered from the patterns forms and parameters of every attribute which most influence the students' length of study period. The result shows that the highest accuracy method is Naïve Bayes Algorithm with accuracy degree of 80.67%. Discussion of this paper emphasizes on the variables that influence the students' study period.