Deep Learning for Sea Turtle Classification: A Bibliometric Analysis Using VOSviewer - NurFarahim, Siti; Shabil, Azran; Sabrina Balqis, Nur (International Journal of Informatics, Information System and Computer Engineering (INJIISCOM), 2024)
Deep learning in the context of sea turtles constitutes a significant area of exploration within marine science. Consequently, the aim of this study is to perform a bibliometric analysis focusing on the subject of sea turtle deep learning, leveraging mapping analysis through the utilization of VOSviewer software. For this research, we employed a bibliometric and descriptive quantitative approach. The data was acquired by conducting a search on Google Scholar using the keyword "Sea Turtle Deep Learning," which yielded a total of 880 articles published between 2018 and 2023. Notably, only 19 of these articles were directly relevant to the research topic. The findings underscore the diversity of research outcomes in the realm of sea turtle deep learning over this time span. In conclusion, this investigation underscores the significance of conducting bibliometric analyses, particularly within the domain of sea turtle deep learning, and serves as a valuable reference for future research endeavours in defining research themes