Classification of engineering journals quartile using Various Supervised Learning models


Nastiti Susetyo Fanany Putri(1); Aji Prasetya Wibawa(2*); Harits Ar Rasyid(3); Anik Nur Handayani(4); Andrew Nafalski(5); Edinar Valiant Hawali(6); Jehad A.H. Hammad(7);

(1) Universitas Negeri Malang
(2) Universitas Negeri Malang
(3) Universitas Negeri Malang
(4) Universitas Negeri Malang
(5) University of South Australia
(6) Universitas Negeri Malang
(7) Al-Quds Open University
(*) Corresponding Author

  

Abstract


In scientific research, journals are among the primary sources of information. There are quartiles or categories of quality in journals which are Q1, Q2, Q3, and Q4. These quartiles represent the assessment of journal. A classification machine learning algorithm is developed as a means in the categorization of journals. The process of classifying data to estimate an item class with an unknown label is called classification. Various classification algorithms, such as K-Nearest Neighbor (KNN), Naïve Bayes, and Support Vector Machine (SVM) are employed in this study, with several situations for exchanging training and testing data. Cross-validation with Confusion Matrix values of accuracy, precision, recall, and error classification is used to analyzed classification performance. The classifier with the finest accuracy rate is KNN with average accuracy of 70%, Naïve Bayes at 60% and SVM at 40%. This research suggests assumption that algorithms used in this article can approach SJR classification system.

Keywords


Quartile Journals; Classification; KNN; Naïve Bayes; SVM

  
  

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doi  https://doi.org/10.33096/ilkom.v15i1.1483.101-106
  

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Copyright (c) 2023 Nastiti Susetyo Fanany Putri, Aji Prasetya Wibawa, Harits Ar Rasyid, Anik Nur Handayani, Andrew Nafalski, Edinar Valiant Hawali, Jehad A.H. Hammad

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