AccScience Publishing / IJOSI / Volume 8 / Issue 1 / DOI: 10.6977/IJoSI.202403_8(1).0003
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Strengthening research partner collaboration in higher education for searching innovation through machine learning-based recom-mender system

Mochamad Nizar Palefi Ma’ady1
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1 Department of Information Systems, Institut Teknologi Telkom Surabaya, Indonesia
IJOSI 2024 , 8(1), 27–35; https://doi.org/10.6977/IJoSI.202403_8(1).0003
Submitted: 3 June 2023 | Revised: 30 November 2023 | Accepted: 30 November 2023 | Published: 22 February 2024
© 2024 by the Author(s). Licensee AccScience Publishing, USA. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC BY-NC 4.0) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Academic collaboration is tremendously important for higher education. Multidisciplinary academicians may be grouped as a better research collaboration than the previous one. Therefore, such a system is needed, even for a huge number of academicians in an institution. However, existing such recommendation tools are expensive. This paper suggests to develop a system by using machine learning approach in order to search a big academicians data effectively. Hence, with help of the standard of Naïve Bayes creates a flexible text search without depending on what select options including research location or case study instead of only research topic. Furthermore, the output of Naïve Bayes, then, is tranformed to percentage display in order to bring ease of understanding the gap of rec-ommendation. It allows the user to choose a possible partner more than one. Therefore, this approach helps reduce time and effort.

Keywords
Higher Education
Naïve Bayes Algorithm
Recommendation System
Research Partner Collaboration
Sigmoid Activation Function
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International Journal of Systematic Innovation, Electronic ISSN: 2077-8767 Print ISSN: 2077-7973, Published by AccScience Publishing
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