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All Journal ILKOM Jurnal Ilmiah
Edinar Valiant Hawali
Universitas Negeri Malang
Computer Science & IT

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Classification of Engineering Journals Quartile using Various Supervised Learning Models Nastiti Susetyo Fanany Putri; Aji Prasetya Wibawa; Harits Ar Rasyid; Anik Nur Handayani; Andrew Nafalski; Edinar Valiant Hawali; Jehad A.H. Hammad
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1483.101-106

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.
Co-Authors Aji Prasetya Wibawa Andrew Nafalski Anik Nur Handayani Harits Ar Rasyid Jehad A.H. Hammad Nastiti Susetyo Fanany Putri