Turkish Journal of Computer and Mathematics Education
Journal license

Journal

Turkish Journal of Computer and Mathematics Education


Volume
& Issue

Volume 12, Issue 3


Published
on

April 5, 2021


Pages

4443-4449


DOI

Article

Social Network Extraction Unsupervised


Authors

Mahyuddin K. M. Nasution* Affiliation:
Information Technology Study Program, Fakultas Ilmu Komputer dan Teknologi Informasi, Universitas Sumatera Utara, Medan 20155, Indonesia
and Rahmad Syah Affiliation:
Department of Informatics, Universitas Medan Area, Medan, Indonesia


Abstract

In the era of information technology, the two developing sides are data science and artificial intelligence. In terms of scientific data, one of the tasks is the extraction of social networks from information sources that have the nature of big data. Meanwhile, in terms of artificial intelligence, the presence of contradictory methods has an impact on knowledge. This article describes an unsupervised as a stream of methods for extracting social networks from information sources. There are a variety of possible approaches and strategies to superficial methods as a starting concept. Each method has its advantages, but in general, it contributes to the integration of each other, namely simplifying, enriching, and emphasizing the results.


Keywords

Superficial method, similarity, occurrence, co-occurrence, search engine, hit count, big data, information source


Citation

Nasution, M. K. M. & Syah, R. (2021). Social network extraction unsupervised. Turkish Journal of Computer and Mathematics Education, 12(3), 4443–4449.

Published by: Engineering Journals

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