Open AccessThesis Supervisor Recommendation with Representative Content and Information Retrieval
Author(s) -
Maresha Caroline Wijanto,
Rachmi Rachmadiany,
Oscar Karnalim
Publication year - 2020
Publication title -
journal of information systems engineering and business intelligence/journal of information systems engineering and business intelligence
Language(s) - English
Resource type - Journals
eISSN - 2598-6333
pISSN - 2443-2555
DOI - 10.20473/jisebi.6.2.143-150
Subject(s) - supervisor , cosine similarity , computer science , information retrieval , collaborative filtering , field (mathematics) , task (project management) , scalability , vector space model , recommender system , similarity (geometry) , data mining , artificial intelligence , mathematics , database , pattern recognition (psychology) , engineering , systems engineering , political science , pure mathematics , law , image (mathematics)
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.