DOI: https://doi.org/10.14710/lenpust.v10i2.68282

Robust Clustering of Open Access Journal Based on Scopus Journal Metrics Database

*Rizki Agung Wibowo  -  Department of Management, Faculty of Economics and Management, Malayahati University, Lampung, Indonesia
Khoirin Nisa orcid scopus  -  Department of Mathematics, Faculty of Mathematics and Natural Science, University of Lampung, Lampung, Indonesia
Amril Samosir  -  Department of Management, Faculty of Economics and Management, Malayahati University, Lampung, Indonesia
Received: 19 Oct 2024; Revised: 27 Oct 2024; Accepted: 30 Dec 2024; Published: 31 Dec 2024.

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Abstract

Background: Open-access is free online access to articles, journal, conferences proceedings, book series and trade journal which provides unrestricted and permit the users to read, download, print, copy and link to the articles. Many articles that discuss the journal metrics using basic statistical methods to discribe the journal.

Objective: This research groups journals based on numerical quality measures, identifying quality characteristics for each group. The findings provide a reference for researchers to select suitable journals and for journal owners to improve journal quality.

Methods: There is another method to describe the open-access journal by grouping it into groups with the homogeneous characteristics based on five types of numerical quality measure that are analyzed simultaneously, namely cluster analysis. By using cluster analysis, the article’s owner can determine which journals he can choose to publish it in according to the desired journal quality. Based in the result, 5146 open-access journals can be divided into four clusters by using CLARA algorithm. Cluster 1, 2 and 3 have high characteristics in all numerical quality measure and cluster 4 have low characteristics in all numerical quality measure. So that researchers can choose journals in clusters 1, 2, and 3 as a place to publish their research by adjusting the journal's scope.

Results: This study demonstrates that the CLARA algorithm successfully grouped 5146 open-access journals indexed by SCOPUS into four clusters based on quality characteristics. Cluster 1 consists of 39 journals with high values across all quality variables, Cluster 2 includes 50 journals with similarly high values, Cluster 3 contains 430 journals with comparable characteristics, and Cluster 4, comprising 4627 journals, exhibits low values in all quality variables. Furthermore, the majority of journals (89.914%) have numerical quality measures below the average.

Conclusion: This study concludes that journals in Clusters 1, 2, and 3 can be recommended as suitable options for researchers to publish their work, considering the relevance of the journal's scope. Additionally, these findings can serve as a reference for journal owners to improve the quality of their journals to meet higher standards.

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Keywords: Open-Access; SCOPUS, Robust, Clustering, CLARA

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Language : EN
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