Özet:

Abstract Bibliometric research has an important role in identifying trends, topics, and influences of a field of study through quantitative analysis of scientific publications. One of the popular data grouping algorithms in bibliometric analysis is Fuzzy C-Means (FCM). However, most bibliometric studies using FCM rely only on grouping the data without conducting further analysis. Therefore, this study aims to apply bibliometric analysis to FCM using VOSviewer. The data for this study were taken from the Scopus database and selected using predetermined selection criteria. A total of 103 documents were selected for analysis using FCM and VOSviewer. The results of the analysis show that FCM can group scientific publications related to Fuzzy C-Means into four groups. These groups were further analyzed using VOSviewer to identify the main topics and relationships between topics. Bibliometric analysis shows that the most dominant topic in FCM research is the application in image processing, with sub-topics such as pixel grouping and image segmentation. In addition, the results of the analysis also show that there is a close relationship between the topic of FCM and the topic of natural language processing and fuzzy logic. This study shows that FCM has great potential in bibliometric analysis, especially in classifying and identifying the main topics of scientific publications. The use of VOSviewer in the bibliometric analysis also helps in describing and visualizing the analysis results more clearly and easily understood. This research can pave the way for further research on the application of FCM in other fields of study as well as the development of more sophisticated and effective methods of bibliometric analysis.

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