EduFinBuddy: A Web-Based Education Financial Aid Recommendation System Using Web Scraping

Education plays a crucial role in shaping Malaysia's future development. However, access to higher education remains discriminatory, particularly for students from lower-income families, individuals with disabilities, and indigenous backgrounds, as they face challenges due to escalating living costs. The Malaysian government has initiated various education financial aid programmes to address this issue, encompassing scholarships, loans, and one-off aid. This initiative aligns with the broader goal of promoting inclusivity and ensuring all aspiring students have equal access to pursue their educational dreams. However, in many cases, some students apply for educational financial aid only to face rejection due to unmet requirements. Therefore, this paper proposes a responsive web-based system, namely EduFinBuddy, that will be able to identify the most suitable education financial aid based on student input. We begin the development process by extracting data from the targeted websites using a web scraping approach and storing it in a database. Then, we build the recommendation component using the cosine similarity algorithm to ensure accuracy based on the student's input. Thus, the student will get a list of educational financial aids according to their preference. The result shows that all the functionalities work well and can be implemented in any educational system of financial assistance to allow easier access to information for tertiary-level students in Malaysia. This initiative aligns with the broader goal of promoting inclusivity and ensuring all aspiring students have equal access to pursue their educational dreams.