Bibliometric Analysis on Kernel Regression Models: Identification of Research Gaps and Future Development Directions
DOI:
https://doi.org/10.55927/ajns.v4i2.30Keywords:
Bibliometric analysis , Gaps research, Open knowledge maps, Kernel Regression, VOSviewerAbstract
Bibliometric analysis was conducted to identify trends and gaps in research related to kernel regression models. The data of this research is sourced from Scopus metadata from 2000-2025. The co-word analysis method was applied to determine a pattern of relationship between keywords in research, with the Vosviewer visualization tool used to visualize a relationship between keywords and areas that are often researched. Based on the results of the analysis, it is stated that topics such as “kernel density estimation” and “kernel metdhods” have been widely researched, while topics such as “kernel learning” and “Bayesian optimization” tend to be little of rarely researched, which means that there is significant research gap. The keyword co-occurrence analysis result show that there have been many articles on the application of kernel regression in health-related research, especially on the topics of “breasth cancer” and “immunothereapy”. Further research on less researched topics is expected to fill the research gap and open up an opportunity for the creation of a new method and new applications in kernel regression. It also provides new insights for readers and researchers to formulate more effective and innovative research strategies in the future.
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Copyright (c) 2025 Khalifatur Rivan Akbar, Dewi Retno Sari Saputro, Purnami Widyaningsih
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