Expert locator system for finding co-authors using snowball network method: A case study of fasilkom ui lecturers

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

One of the essential activities for academics is research. Research is typically done through collaborations. One way to find research collaborators is through existing connections, for example, via the co-author relationships. However, existing systems only provide limited information on collaborators. Furthermore, previous works have not fully explored the details of co-author relationships. Hence, we propose an Expert Locator System (ELS) that can identify collaborators and provide information on the location, publication keywords, as well as their impact and productivity. We can obtain the relationship using the snowball network method, a type of Social Network Analysis (SNA). This ELS can serve to obtain collaborators' information that can be useful in conducting research.

Original languageEnglish
Title of host publicationICEMT 2020 - 2020 the 4th International Conference on Education and Multimedia Technology
Number of pages8
PublisherAssociation for Computing Machinery, Inc.
Publication date19 Jul 2020
Pages6-13
ISBN (Electronic)9781450388375
DOIs
Publication statusPublished - 19 Jul 2020
Externally publishedYes
Event4th International Conference on Education and Multimedia Technology, ICEMT 2020 - Virtual, Online, Japan
Duration: 19 Jul 202022 Jul 2020

Conference

Conference4th International Conference on Education and Multimedia Technology, ICEMT 2020
LandJapan
ByVirtual, Online
Periode19/07/202022/07/2020
SeriesACM International Conference Proceeding Series

Bibliographical note

Funding Information:
This work is supported by the Publikasi Terindeks Interna-sional (PUTI) Prosiding Universitas Indonesia 2020 grant (NKB-873/UN2.RST/HKP.05.00/2020).

Publisher Copyright:
© 2020 ACM.

    Research areas

  • Expert locator system, H-index, Knowledge management, Knowledge sharing system, Publication keyword

ID: 320796324