Adaptation of Hybrid Filtering in Recommending Degree Course for Diploma Students

Education is a crucial aspect of human evolution; decades of technical progress can be attributed to the advancement and collection of information. More education years are expected to increase production and thus income by offering information, skills, and a problem-solving mindset. Thus, having higher education would present a better work opportunity. Every year, the number of dropouts has increased significantly, one of the factors being students struggle to choose a program that is suitable for them. The effects can be seen in depressive symptoms in students due to the stress of education. This project is developed to provide a method of recommending degree program for diploma students to help overcome the problem. Students can make more informed judgements and boost their chances of academic achievement by providing a recommendation system based on program details. The project implements content-based filtering to produce recommendations based on the program that is offered in the Faculty of Computer and Mathematical Sciences. Modified waterfall methodology is used as the Software Development Life Cycle methodology to make sure that the system is completed and achieves the objectives that were set. The system uses the program details for five programs as the item for the content-based filtering algorithm. The input for the system is taken from the user’s input from answering a set of questionnaires. Functionality testing showed that the system can produce a baseline recommendation for the user, with the recommended programs ranked in order of the highest to lowest likelihood probability.