Thesis Supervisor Recommendation with Representative Content and Information Retrieval

Clicks: 222
ID: 261145
2020
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Abstract
Background: In higher education in Indonesia, students are often required to complete a thesis under the supervision of one or more lecturers. Allocating a supervisor is not an easy task as the thesis topic should match a prospective supervisor’s field of expertise. Objective: This study aims to develop a thesis supervisor recommender system with representative content and information retrieval. The system accepts a student thesis proposal and replies with a list of potential supervisors in a descending order based on the relevancy between the prospective supervisor’s academic publications and the proposal. Methods: Unique to this, supervisor profiles are taken from previous academic publications. For scalability, the current research uses the information retrieval concept with a cosine similarity and a vector space model. Results: According to the accuracy and mean average precision (MAP), grouping supervisor candidates based on their broad expertise is effective in matching a potential supervisor with a student. Lowercasing is effective in improving the accuracy. Considering only top ten most frequent words for each lecturer’s profile is useful for the MAP. Conclusion: An arguably effective thesis supervisor recommender system with representative content and information retrieval is proposed.  
Reference Key
wijanto2020journalthesis Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Maresha Caroline Wijanto;Rachmi Rachmadiany;Oscar Karnalim;
Journal journal of information systems engineering and business intelligence
Year 2020
DOI
10.20473/jisebi.6.2.143-150
URL
https://e-journal.unair.ac.id/JISEBI/article/view/21241
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