Naïve Bayes Classifier for Journal Quartile Classification

Authors

  • Aji Prasetya Wibawa Electrical Engineering Department Universitas Negeri Malang Malang, Indonesia https://orcid.org/0000-0002-6653-2697
  • Ahmad Chandra Kurniawan Electrical Engineering Department Universitas Negeri Malang Malang, Indonesia
  • Della Murbarani Prawidya Murti Electrical Engineering Department Universitas Negeri Malang Malang, Indonesia
  • Risky Perdana Adiperkasa Electrical Engineering Department Universitas Negeri Malang Malang, Indonesia
  • Sandika Maulana Putra Electrical Engineering Department Universitas Negeri Malang Malang, Indonesia
  • Sulton Aji Kurniawan Electrical Engineering Department Universitas Negeri Malang Malang, Indonesia
  • Youngga Rega Nugraha Electrical Engineering Department Universitas Negeri Malang Malang, Indonesia

DOI:

https://doi.org/10.3991/ijes.v7i2.10659

Abstract


Classification is a process for distinguishing data classes, with the aim of being able to estimate the class of an object with unknown label. One popular method that used for classifying data is Naïve Bayes Classifier. Naïve Bayes Classifier is an approach that adopts the Bayes theorem, by combining previous knowledge with new knowledge. The advantages of this method are the simple algorithm and high accuracy. In this study, it will show the ability of Naïve Bayes Classifier to classify the quality of a journal commonly called Quartile. This study use a dataset of 1491 instances. The results show an accuracy of 71.60% and an error rate of 28.40%

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Published

2019-06-21

How to Cite

Wibawa, A. P., Kurniawan, A. C., Murti, D. M. P., Adiperkasa, R. P., Putra, S. M., Kurniawan, S. A., & Nugraha, Y. R. (2019). Naïve Bayes Classifier for Journal Quartile Classification. International Journal of Recent Contributions from Engineering, Science & IT (iJES), 7(2), pp. 91–99. https://doi.org/10.3991/ijes.v7i2.10659

Issue

Vol. 7 No. 2 (2019)

Section

Short Papers

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