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

International Conference on Engineering and Technology for Sustainable Development 2015

工业技术;经济学

Harwati^1 ; Virdyanawaty, Riezky Ikha^1 ; Mansur, Agus^1

Industrial Engineering Department, Faculty of Industrial Technology Universitas Islam Indonesia, Jl. Kaliurang km. 14, 5, Yogyakarta, Indonesia^1

关键词: Accuracy degree;    Accuracy level;    Bayes algorithms;    Drop-out;    Industrial technology;    Nave bayes;    Support vector machines algorithms;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/105/1/012039/pdf
DOI  :  10.1088/1757-899X/105/1/012039

学科分类：工业工程学

来源: IOP

【 摘 要 】

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. Naï ve 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 Naï ve Bayes Algorithm with accuracy degree of 80.67%. Discussion of this paper emphasizes on the variables that influence the students' study period.