| dc.description.abstract | The problem of analysing publication citation networks has recently been widely
researched, but many studies only analyse one or several types of journals due to
differences in the metadata of each journal. Apart from that, there are also those who only
count the number of citations without taking into account the distribution of citations,
which includes how far apart they are based on the distance between the author's university
and the university of the author citing the publication. This research analyses the
publication citations of University of North Sumatra lecturers from Google Scholar using
a text mining approach, and then the text mining results obtained visualise the distribution
of publication citations through the publication citation network formed from each node.
Due to the complexity of the network formed, including differences in metadata for each
journal and the wide distribution of publication sources, it is difficult to build network
citations. This research aims to build a visualization model of the distribution of lecturer
publications at the Universitas Sumatera Utara. Text mining was performed by web
scraping on Google Scholar with Python. The data used is lecturer data on the SINTAScience
and Technology Index website, which includes publication data from lecturers at
the University of North Sumatra, consisting of 1730 records. The method used is a
quantitative approach with computational text mining and web scraping algorithms. Social
Network Analysis (SNA) is used to build the network formed between authors. The
research results show that the proposed method can solve the problem of publication
citation network visualization. The resulting visualisation of network citation publications
can provide valuable knowledge about the structure and patterns of connectivity in network
citation publications. | en_US |
