Big Data Analytics (BDA) in the Research Landscape: Using Python and VOSviewer for Advanced Bibliometric Analysis
Samsul Arifin1, Muhammad Faisal1, Monica Mayeni Manurung1, Bakti Siregar1, Andi Pujo Rahadi1, Abdullah Eli2, Gilang Ramadhan2 & Ilham Fikriansyah2 Show affiliations
- Department of Data Science, Faculty of Engineering and Design, Institut Teknologi Sains Bandung, Bekasi, West Java, Indonesia
- Miningtech BC Research Team, PT Berau Coal, Tanjung Redeb, Berau, Kalimantan Timur, Indonesia
Journal of Computer Science, Volume 21, Issue 2, pp. 347–362 · doi.org/10.3844/jcssp.2025.347.362
Abstract
Big data analytics has become a key element in research and development in various fields. With the ability to analyze large and complex amounts of data, this technology allows researchers to identify patterns, trends, and insights that were not seen before. This article explores how big data analytics is applied in various disciplines, including computer science, engineering, and mathematics. We use Python for data processing and analysis, as well as VOSviewer for in-depth bibliometric visualization. The study highlights recent developments in big data analysis methodologies, the challenges they face, and the potential for the future. Our findings suggest that the integration of advanced analytical techniques can accelerate scientific discovery and improve understanding across different research domains.
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Open access. This article is licensed under a Creative Commons Attribution 4.0 International Licence (CC BY 4.0), as printed on the article, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source. No permission is needed.
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Arifin, S., Faisal, M., Manurung, M. M., Siregar, B., Rahadi, A. P., Eli, A., Ramadhan, G. & Fikriansyah, I. (2025). Big Data Analytics (BDA) in the Research Landscape: Using Python and VOSviewer for Advanced Bibliometric Analysis. Journal of Computer Science, 21(2), 347–362. https://doi.org/10.3844/jcssp.2025.347.362
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