Content-Based Filtering Using TF-IDF for a Course Recommendation System for Indonesian MSME Entrepreneurs

Abstract

Micro, Small, and Medium Enterprises (MSMEs), particularly those led by female entrepreneurs, play a vital role in Indonesia’s economic development; however, digital learning platforms often lack adaptive mechanisms that align course offerings with individual learning needs. Existing platforms generally rely on manual selection or generic categorization, creating a gap in personalized recommendation support within digital entrepreneurship education. The primary objective of this study is to develop and assess a content-based course recommendation system for Femalepreneur.id that addresses this limitation. A quantitative experimental research design was adopted using user profile data collected through questionnaires and course descriptions obtained from the platform repository. The methodology integrates systematic text preprocessing, feature representation using Term Frequency–Inverse Document Frequency (TF-IDF), and similarity computation through cosine similarity to generate personalized recommendations. The experimental results indicate Mean Precision@3 at 51.5%, Mean Average Precision@3 (MAP@3) at 42.9%, and Hit Ratio@3 at 90.9%. The precision matrix demonstrates the system can recommend relevant result until three courses as the maximum value based on the ground truth.  While, Hit Ratio matrix reveals that at least the system can recommend at least one relevant topic.  These findings confirm the effectiveness of TF-IDF in modelling textual learning features and highlight the contribution of the proposed system in strengthening personalized digital entrepreneurship learning for female entrepreneurs.