Improving the Platform in the Lecture Scope with the Implementation of the TF-IDF Algorithm

Abstract

 Information plays a crucial role in the lives of everyone, including students. One of the most important 
types of information that students need is related to lecture events. However, students often have difficulty finding 
suitable lecture event information, and they sometimes forget to attend events they have registered for. As a result, 
this research focuses on designing and implementing a website-based recommendation system for lectures. The 
recommendation system utilizes the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm in its 
development. The primary goal of developing this recommendation system is to help students easily find event 
information and receive event activity notifications. In alignment with the established objectives, a significant number 
of respondents agree that the recommendation system simplifies event registration. Consequently, with the aid of this 
system, students can become more proactive and participate more readily in events. Following a survey conducted 
with 47 respondents, it was found that 84.4% of them had never used a recommendation system for college activities. 
Furthermore, 53.1% expressed satisfaction with this recommendation system. It is hoped that the recommendation 
system, employing the TF-IDF algorithm, can be further optimized to yield even better results.