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Andri Riyan Dwi Putra Siregar
Muhammadiyah University of North Sumatra Medan
Computer Science & IT Economics, Econometrics & Finance Education Engineering Social Sciences

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Mood-Based Song Recommendation System Using TF-IDF and Cosine Similarity Andri Riyan Dwi Putra Siregar; Rizaldy Khair
Journal of Scientific Research, Education, and Technology (JSRET) Vol. 5 No. 4 (2026): Vol. 5 No. 4 2026
Publisher : Kirana Publisher (KNPub)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58526/jsret.v5i4.1272

Abstract

This study created a mood-based music recommendation system using a content-based filtering approach with TF-IDF and Cosine Similarity to handle the huge amount of digital music data. Using about 15,000 songs from Kaggle, the system processes metadata and audio features like valence, energy, danceability, and tempo, all implemented in a Python and Flask web app. The mix of text similarity and audio features works really well for giving spot-on recommendations, as shown by performance scores of 0.97 accuracy, 0.90 precision, 0.82 recall, and 0.86 F1-score. Plus, with a System Usability Scale (SUS) rating in the good range, the system is not just technically accurate but also super easy to use and well-liked by users.
Co-Authors Rizaldy Khair