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Development of Educational Data Mining Model for Predicting Student Punctuality and Graduation Predicate

Author

Listed:
  • Rianto

    (Universitas Teknologi Yogyakarta, Yogyakarta, Indonesia)

  • Muhammad Fachrie

    (Universitas Teknologi Yogyakarta, Yogyakarta, Indonesia)

Abstract

This paper discusses the Educational Data Mining (EDM) to predict the punctuality and graduation predicate. Both are considered as important aspects that represent the student’s academic performance. The model was developed by using academic records of 100 students from the vocational school of Informatics Management at Universitas Teknologi Yogyakarta. The dataset consisting of three features and two different labels was obtained by creating an Application Programming Interfaces (APIs) that connected to an academic database. Two classification algorithms were used to obtain knowledge from the dataset, i.e., Support Vector Machine (SVM) and Naive Bayes (NB). From the observations, SVM achieved the level of accuracy for punctuality of graduation on 0.68 while NB on 0.60. On graduation predicate, both algorithms achieved the same accuracy level on 0.92.

Suggested Citation

  • Rianto & Muhammad Fachrie, 2019. "Development of Educational Data Mining Model for Predicting Student Punctuality and Graduation Predicate," International Journal of Technology and Engineering Studies, PROF.IR.DR.Mohid Jailani Mohd Nor, vol. 5(5), pages 151-156.
    • Handle: RePEc:apa:ijtess:2019:p:151-156
    DOI: 10.20469/ijtes.5.10002-5
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    References listed on IDEAS

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    1. Christine Lourrine S. Tablatin & Frederick F. Patacsil & Paulo V. Cenas, 2016. "Design and development of an information technology fundamentals multimedia courseware for dynamic learning environment," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 2(6), pages 202-210.
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