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.

Citation Format:
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.

Fulltext View|Download
Keywords: Open-Access; SCOPUS, Robust, Clustering, CLARA

Article Metrics:

Article Info
Section: Articles
Language : EN
Recent articles
Information Anxiety: A Phenomenon Experienced by Final Year Students During the Preparation of Their Thesis Pemanfaatan Koleksi Perpustakaan dalam Tesis dan Disertasi Mahasiswa Pascasarjana UIN Sunan Ampel: Studi Analisis Sitasi Analisis Penerimaan Informasi Kekayaan Intelektual dan Akuntabilitas Dokumen (INTIPDAQU) oleh Arsiparis Badan Riset dan Inovasi Nasional (BRIN) menggunakan Technology Acceptance Model (TAM) More recent articles
Most cited articles
Backmatter Frontmatter Backmatter Frontmatter Backmatter More cited articles
  1. Brock, G., Pihur, V., Datta, S., & Datta, S. (2008). clValid: An R Package for Cluster Validation. Journal of Statistical Software, 25(4 SE-Articles), 1–22. https://doi.org/10.18637/jss.v025.i04
  2. Burnham, J. F. (2006). Scopus database: A review. Biomedical Digital Libraries, 3(1), 1–8. https://doi.org/10.1186/1742-5581-3-1/TABLES/2
  3. Devi, P., & Kaur, K. (2014). A Robust Cluster Head Selection Method Based on K-Medoids Algorithm to Maximize Network Life Time and Energy Efficiency for Large WSNs. INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT), 3(5), 1430–1432
  4. Gupta, T., Gupta, T., & Panda, S. P. (2019). A Comparison of K-Means Clustering Algorithm and CLARA Clustering Algorithm on Iris Dataset. International Journal of Engineering & Technology, 7(4), 4766–4768. https://doi.org/10.14419/ijet.v7i4.21472
  5. Gupta, T., & Panda, S. P. (2019). Clustering Validation of CLARA and K-Means Using Silhouette DUNN Measures on Iris Dataset. Proceedings of the International Conference on Machine Learning, Big Data, Cloud and Parallel Computing: Trends, Prespectives and Prospects, COMITCon 2019, 10–13. https://doi.org/10.1109/COMITCON.2019.8862199
  6. Hair, J. F., Black, W. C., & Anderson, R. E. (2014). Multivariate Data Analysis: Pearson New International Edition (7th ed.). England: Pearson Education Limited
  7. J., A., Prakash, M., & Balasubramani, R. (2021). A Study on Contribution of Open Access Journals on Robotics in Directory of Open Access Journals (DOAJ) Platform. Department of Library and Information Science
  8. Jin, X., & Han, J. (2017). K-Means Clustering. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining (pp. 695–697). Boston, MA: Springer US. https://doi.org/10.1007/978-1-4899-7687-1_431
  9. Larasati, S. D. A., Nisa, K., & Herawati, N. (2021). Robust Principal Component Trimmed Clustering of Indonesian Provinces Based on Human Development Index Indicators. Journal of Physics: Conference Series, 1751(1), 0–8. https://doi.org/10.1088/1742-6596/1751/1/012021
  10. Puspita, A. T. (2021). Comparing between Scopus, Web of Science and Dimensions Indexation: Case of 100 Most Cited Articles on Waqf. Journal of Islamic Economic Literatures, 2(2)
  11. Schubert, E., & Rousseeuw, P. J. (2019). Faster -Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11807 LNCS, 171–187. https://doi.org/10.1007/978-3-030-32047-8_16
  12. Scopus preview - Scopus - Sources. (n.d.). Retrieved April 9, 2022, from https://www.scopus.com/sources?zone=TopNavBar&origin=NO ORIGIN DEFINED
  13. Shang, R., Ara, B., Zada, I., Nazir, S., Ullah, Z., & Khan, S. U. (2021). Analysis of Simple K- Mean and Parallel K- Mean Clustering for Software Products and Organizational Performance Using Education Sector Dataset. Scientific Programming, 2021. https://doi.org/10.1155/2021/9988318
  14. Suharjo, B., & Utama, M. S. U. (2021). K-Means Cluster Analysis of Sex, Age, and Comorbidities in the Mortalities of Covid-19 Patients of Indonesian Navy Personnel. JISA(Jurnal Informatika Dan Sains), 4(1), 17–21. https://doi.org/10.31326/JISA.V4I1.869
  15. Venelia, H., Nisa, K., Wibowo, R. A., & Muda, M. A. (2021). Robust Biplot Analysis of Natural Disasters in Indonesia from 2019 to 2021. Jurnal Aplikasi Statistika & Komputasi Statistik, 13(2), 61–68. https://doi.org/10.34123/jurnalasks.v13i2.349
  16. Vijayan, D. S., R, R. V, & R, A. V. (2021). Web of Science (WoS) Indexed Library and Information Science (LIS) Journals in Scopus: An Analysis. Library Philosophy and Practice (e-Journal). Retrieved from https://digitalcommons.unl.edu/libphilprac/6348
  17. Wu, C., Yan, B., Yu, R., Yu, B., Zhou, X., Yu, Y., & Chen, N. (2021). K -Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform. Complexity, 2021. https://doi.org/10.1155/2021/9446653

Last update:

No citation recorded.

Last update: 2025-01-15 20:53:41

No citation recorded.