Mood-Based Song Recommendation System Using Sugeno Fuzzy Logic

Anandava Eka Buana Baskara; Faiz Ahmad Fauzan; Rizka Octa Setiani; Cintiya Cintiya; Imiel Ardhanenggar Tallane; Fadil Indra Sanjaya

doi:10.55927/fjmr.v5i6.111

Authors

Anandava Eka Buana Baskara Universitas Teknologi Yogyakarta
Faiz Ahmad Fauzan Universitas Teknologi Yogyakarta
Rizka Octa Setiani Universitas Teknologi Yogyakarta
Cintiya Cintiya Universitas Teknologi Yogyakarta
Imiel Ardhanenggar Tallane Universitas Teknologi Yogyakarta
Fadil Indra Sanjaya Universitas Teknologi Yogyakarta

DOI:

https://doi.org/10.55927/fjmr.v5i6.111

Keywords:

Music Recommendation, Sugeno Fuzzy Logic, Spotify Audio Features, Mood Prediction

Abstract

Digital music platforms expose listeners to large catalogs, making mood-oriented filtering useful for music discovery. This study built a content-based song recommender that maps Spotify audio features to five mood levels using zero-order Sugeno fuzzy logic. The model uses valence, energy, tempo, and mode from SpotifyFeatures.csv. Triangular membership functions activate five rules whose consequents represent very sad, sad, neutral, happy, and very happy. The resulting mood score is used for both classification and ranking songs by distance from a selected mood target. The prototype combines a FastAPI backend and a React interface for recommendation and song search. The work provides an auditable baseline; validation with listener judgments remains necessary.

References

Aldeshev, A., Seitbekov, S., Kartbayev, A., Tynysbekov, P., & Dairov, O. (2025). Harmonizing emotions and music with fuzzy intelligence for personalized recommendations. 2025 IEEE 5th International Conference on Smart Information Systems and Technologies (SIST), 1–5. https://doi.org/10.1109/SIST61657.2025.11139164

Amiri, B., Shahverdi, N., Haddadi, A., & Ghahremani, Y. (2024). Beyond the trends: Evolution and future directions in music recommender systems research. IEEE Access, 12, 51500–51522. https://doi.org/10.1109/ACCESS.2024.3386684

Gomez-Canon, J. S., Gutierrez-Paez, N., Porcaro, L., Porter, A., & Cano, E. (2023). TROMPA-MER: An open dataset for personalized music emotion recognition. Journal of Intelligent Information Systems, 60(2), 549–570. https://doi.org/10.1007/s10844-022-00746-0

Hamidani, Z. (2019). Spotify Tracks DB. https://www.kaggle.com/datasets/zaheenhamidani/ultimate-spotify-tracks-db

Han, D., Kong, Y., Han, J., & Wang, G. (2022). A survey of music emotion recognition. Frontiers of Computer Science, 16(6). https://doi.org/10.1007/s11704-021-0569-4

Jing, E., Liu, Y., Chai, Y., Yu, S., Liu, L., Jiang, Y., & Wang, Y. (2025). Emotion-aware personalized music recommendation with a heterogeneity-aware deep bayesian network. ACM Transactions on Information Systems, 43(5), 1–43. https://doi.org/10.1145/3733233

Lu, J., Ma, G., & Zhang, G. (2024). Fuzzy machine learning: A comprehensive framework and systematic review. IEEE Transactions on Fuzzy Systems, 32(7), 3861–3878. https://doi.org/10.1109/TFUZZ.2024.3387429

Mao, Y., Zhong, G., Wang, H., & Huang, K. (2022). Music-CRN: An efficient content-based music classification and recommendation network. Cognitive Computation, 14(6), 2306–2316. https://doi.org/10.1007/s12559-022-10039-x

Melchiorre, A. B., Penz, D., Ganhor, C., Lesota, O., & Fragoso, V. (2023). Emotion-aware music tower blocks (EmoMTB): An intelligent audiovisual interface for music discovery and recommendation. International Journal of Multimedia Information Retrieval, 12(1). https://doi.org/10.1007/s13735-023-00275-8

Moysis, L., Iliadis, L. A., Sotiroudis, S. P., Boursianis, A. D., & Papadopoulou, M. S. (2023). Music deep learning: Deep learning methods for music signal processing—A review of the state-of-the-art. IEEE Access, 11, 17031–17052. https://doi.org/10.1109/ACCESS.2023.3244620

Panda, R., Malheiro, R., & Paiva, R. P. (2023). Audio Features for Music Emotion Recognition: A Survey. IEEE Transactions on Affective Computing, 14(1), 68–88. https://doi.org/10.1109/TAFFC.2020.3032373

Pandey, A. (2025). Cold-start music recommendation using meta-learning and fuzzy logic: A hybrid approach. Journal of Information Systems Engineering and Management, 10(51s), 86–101. https://doi.org/10.52783/jisem.v10i51s.10370

Pedrycz, W. (1994). Why triangular membership functions? Fuzzy Sets and Systems, 64(1), 21–30. https://doi.org/10.1016/0165-0114(94)90003-5

Sugeno, M., & Kang, G. T. (1988). Structure identification of fuzzy model. Fuzzy Sets and Systems, 28(1), 15–33. https://doi.org/10.1016/0165-0114(88)90113-3

Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, SMC-15(1), 116–132. https://doi.org/10.1109/TSMC.1985.6313399

Tran, H., Le, T., Do, A., Vu, T., Bogaerts, S., & Howard, B. (2024). Emotion-aware music recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16087–16095. https://doi.org/10.1609/aaai.v37i13.26911

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X