Predicting and Analyzing the Length of Study-Time Case Study: Computer Science Students

Authors

  • Teny Handhayani Universitas Tarumanagara
  • Lely Hiryanto Universitas Tarumanagara

DOI:

https://doi.org/10.21512/comtech.v8i2.3756

Keywords:

Support Vector Machine, Mutual Information, length of study-time

Abstract

The length of study-time is one of the important issues in higher education. The goal of this research was to predict and analyze the length of studytime in the early stage of Computer Science students in X University. The research proposed Mutual Information (MI) as feature selection method and Support Vector Machine (SVM) as a classification method. There were two different sections of the experiments. The first experiment used two class targets that were grouped in ‘on time group’ and ‘late group’. The experiment result shows that the proposed method produces accuracy around 85%. The second experiment used three class targets, ‘on time group’, ‘late group’, and ‘very late group’. The experiment result of the proposed method produces accuracy around 80%. Mutual Information (MI) does not only successfully raise the accuracy but also uncovers the relationship between subjects and the class targets.

Dimensions

Plum Analytics

Author Biographies

Teny Handhayani, Universitas Tarumanagara

Department of Computer Science

Lely Hiryanto, Universitas Tarumanagara

Department of Computer Science

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Published

2017-06-30

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