Open AccessRecommendation System for Thesis Topics Using Content-based Filtering
Author(s) -
Hans Satria Kusuma,
Aina Musdholifah
Publication year - 2021
Publication title -
indonesian journal of computing and cybernetics systems
Language(s) - English
Resource type - Journals
eISSN - 2460-7258
pISSN - 1978-1520
DOI - 10.22146/ijccs.62716
Subject(s) - recommender system , novelty , computer science , serendipity , relevance (law) , bachelor , process (computing) , syllabus , information retrieval , world wide web , diversity (politics) , collaborative filtering , plan (archaeology) , multimedia , mathematics education , psychology , social psychology , history , philosophy , archaeology , epistemology , political science , law , operating system , sociology , anthropology
When pursuing their bachelor degree, every students are required to pursue a thesis in order to graduate from the major that they take. However, during the process, students got several difficulty regarding chosing their thesis topics. Therefore, a recommendation system is needed to classify thesis topics based on the students’ interest and abilities. This study developed a recommendation system for thesis topics using content-based filtering where the students will be asked to choose the course that they interested in along with their grades. After getting all the required data, the recommendation system will process the data and then it’ll show the title and the abstract of publication that fits the criteria.In this research, there are 2 datasets that is used, there are lecturer publication within 3 years and syllabus data of Computer Science UGM course. After running this research, it was found that the recommendation system has an average 7.46 seconds running time. It was also found that the recommendation system got an average 83% of the recommendation system objectives. The recommendation system objectives consist of relevance, novelty, serendipity, and increasing recommendation diversity.