Machine Learning and Internet of Things (IoT): A Bibliometric Analysis of Publications Between 2012 and 2022


Hamdan Gani(1*); Annisa Dwi Damayanti(2); Nurani Nurani(3); Sitti Zuhriyah(4); St. Nurhayati Jabir(5); Helmy Gani(6); Feng Zhipeng(7); Aisyah Sri Rejeki(8);

(1) Politeknik ATI Makassar
(2) Universitas Hasanuddin
(3) Institut Teknologi dan Bisnis Nobel Indonesia
(4) Universitas Handayani Makassar
(5) Politeknik ATI Makassar
(6) Sekolah Tinggi Ilmu Kesehatan
(7) Hangzhou Normal University
(8) Universitas Hasanuddin
(*) Corresponding Author

  

Abstract


The implementation between machine learning and the Internet of Things (IoT) has been scientifically investigated in many studies. However, not many bibliometric studies categorize the output in this area. By keeping an eye on the publications posted on the Web of Science (WoS) platform, this study aims to give a bibliometric analysis of research on Machine Learning and IoT, identifying the state of the art, trends, and other indicators. 6.170 different articles made up the sample. The VOS viewer software was used to process the data and graphically display the results. The study examined the concurrent occurrence of publications by year, keyword trends, co-citations, bibliographic coupling, and analysis of co-authorship, countries, and institutions. It found several prolific authors, but the body of literature on machine learning and IoT issues is expanding quickly; only five papers accounted for more than 2193 citations. Then, 40.34 percent of the articles from the 694 sources reviewed were published as the most important paper. At the same time, the USA is the top nation for research on this subject area. In addition to identifying gaps and promising areas for future research, this study offers insight into the current state of the art and the field of machine learning and IoT.


Keywords


Bibliometric Analysis, IoT, Machine Learning, Web of Science.

  
     

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doi  https://doi.org/10.33096/ilkom.v16i1.1700.%25p 		  

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