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Article Details

Vol. 1 No. 1 (2026): Februari

Articles

Fuzzy Mamdani-Based Book Recommendation System for Academic Library Services: Design, Implementation, and Evaluation

P Putri Gustina H Hariyanto Wibowo
21 February 2026 Vol. 1 No. 1 (2026): Februari https://doi.org/10.35912/jkbpm.v1i1.6765
Abstract
21 Feb 2026

Purpose: This study aims to develop and evaluate a Fuzzy Mamdani based book recommendation system for the IIB Darmajaya Library to provide personalized recommendations and improve information retrieval efficiency.
Research Methodology: The system was developed through requirement analysis, design, implementation, testing, and evaluation. A Mamdani Fuzzy Inference System (FIS) was constructed using three input variables: borrowing frequency, book rating, and difficulty level, with recommendation score as the output. Twenty-seven fuzzy rules and triangular membership functions were applied. The system was implemented using PHP, Laravel, MariaDB, and JavaScript, while MATLAB was used for FIS verification.
Results: Functional testing confirmed the successful operation of all system modules, including authentication, recommendation generation, search, filtering, and preference updates. Verification results showed complete consistency between manual calculations and MATLAB outputs, with a sample input producing a recommendation score of 4.3.
Conclusions: The proposed system effectively generated personalized book recommendations and demonstrated the capability of the Mamdani method to manage uncertainty in user preferences.
Limitations: The evaluation was limited to functional testing in a controlled environment. User acceptance testing, scalability assessment, and additional recommendation variables were not examined.
Contributions: This study provides a validated Fuzzy Mamdani recommendation framework and a web based implementation model that can be adapted by academic libraries seeking intelligent recommendation services.

Keywords: Academic Library Book Recommendation System Defuzzification Fuzzy Mamdani Fuzzy Inference System
How to Cite
“
Gustina, P. ., & Wibowo, H. . (2026). Fuzzy Mamdani-Based Book Recommendation System for Academic Library Services: Design, Implementation, and Evaluation. Jurnal Kecerdasan Buatan Dan Pembelajaran Mesin, 1(1), 63–74. https://doi.org/10.35912/jkbpm.v1i1.6765
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References
  1. Arfida, S., & Saputra, R. B. (2017). Rancang bangun aplikasi pembelajaran Fuzzy Logic berbasis multimedia. Jurnal Informatika Darmajaya, 17(1), 93-104..
  2. Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109–132. https://doi.org/10.1016/j.knosys.2013.03.012
  3. Burke, R. (2010). Hybrid recommender systems: Survey and experiments. User Modeling and User- Adapted Interaction, 12(4), 331-370. https://doi.org/10.1023/A:1021240730564
  4. Chen, L., & Pu, P. (2012). Critiquing-based recommenders: Survey and emerging trends. User Modeling and User-Adapted Interaction, 22(1–2), 125–150. https://doi.org/10.1007/s11257-011-9 108-6
  5. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
  6. Hakim, A., & Azhar, M. (2019). Penerapan fuzzy logic pada sistem e-library untuk rekomendasi buku berbasis preferensi pengguna. Jurnal Sistem Informasi, 15(2), 45-53..
  7. Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75-105. https://doi.org/10.2307/25148625
  8. Isinkaye, F. O., Folajimi, Y. O., & Ojokoh, B. A. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16(3), 261-273. https://doi.org/10.1016/j.ei j.2015.06.005
  9. Iskandar, R., & Putra, D. (2020). Sistem rekomendasi game menggunakan logika fuzzy Mamdani. Jurnal Teknologi Informasi dan Ilmu Komputer, 7(4), 789-796. https://doi.org/10.25126/jtiik.2020 74812
  10. Jang, J. S. R., Sun, C. T., & Mizutani, E. (1997). Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence.
  11. Lops, P., De Gemmis, M., & Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. In F. Ricci et al. (Eds.), In F. Ricci et al. (Eds.), Recommender Systems Handbook (pp. 73–105). Springer (pp. 73-105).
  12. Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1-13. https://doi.org/10.1016/S002 0-7373(75)80002-2
  13. Nielsen, J. (1994). Usability engineering. Academic Press.
  14. Peska, L., & Vojtas, P. (2017). Fuzzy approach to user preference modeling for recommender systems. Paper presented at In Proceedings of the 8th International Conference on Fuzzy Computation Theory and Applications (pp. 38–47). ScitePress.
  15. Polatidis, N., Georgiadis, C. K., Pimenidis, E., & Mouratidis, H. (2017). Privacy-preserving collaborative recommendations based on random perturbations. Expert Systems with Applications, 71, 18–28. https://doi.org/10.1016/j.eswa.2016.11.018
  16. Putra, R., & Hasan, M. (2020). Sistem rekomendasi restoran menggunakan metode fuzzy Mamdani. Jurnal Informatika, 14(2), 110-118. https://doi.org/10.26555/jifo.v14i2.a16456
  17. Ricci, F., Rokach, L., & Shapira, B. (2015). Recommender systems: Introduction and challenges. In F. Ricci et al. (Eds.), In F. Ricci et al. (Eds.), Recommender Systems Handbook (2nd ed., pp. 1–34). Springer.
  18. Santoso, B., & Dewi, R. (2021). Penerapan logika fuzzy dalam sistem rekomendasi hotel berbasis preferensi pengguna. Jurnal Teknologi dan Sistem Komputer, 9(3), 155-162. https://doi.org/10.147 10/jtsiskom.2021.13799
  19. Sarjanako, R. J., & Utami, M. (2017). Penerapan metode Fuzzy Mamdani untuk rekomendasi optimalisasi penentuan harga sewa kios. Jurnal Ilmiah Teknologi-Informasi dan Sains (TeknoIS), 7(2), 1-12..
  20. Setiawan, A., Rahmawati, D., & Nugraha, F. (2022). Implementasi logika fuzzy untuk rekomendasi kategori buku pada perpustakaan sekolah. Jurnal Ilmu Komputer dan Informatika, 8(1), 22-30..
  21. Sulyono, H., Ridwan, A., & Purnama, B. E. (2022). Sistem rekomendasi kesesuaian skema penelitian dosen berbasis kecerdasan buatan menggunakan algoritma ID3. Jurnal Ilmiah Teknologi Informasi Asia, 16(1), 45-54..
  22. Sutisna, H., Basjaruddin, N. C., & Suryani, E. (2015). Sistem pendukung keputusan pemilihan pekerjaan menggunakan metode Fuzzy Mamdani. Jurnal Teknologi Rekayasa, 2(2), 115-124. Tapus, A., Çiftci, B., & Jain, R. (2022). Explainability in fuzzy recommender systems: A systematic review. Knowledge-Based Systems, 240, 108155. https://doi.org/10.1016/j.knosys.2022.108155
  23. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2022). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 46(1), 1-34. https://doi.org/10.25300/MISQ/2022/14674
  24. Widodo, H., & Haryanto, T. (2018). Sistem rekomendasi buku berbasis logika fuzzy Sugeno di perpustakaan universitas. Jurnal Rekayasa Sistem dan Teknologi Informasi, 2(1), 55-62..
  25. Yulmaini. (2015). Penggunaan metode Fuzzy Inference System (FIS) Mamdani dalam pemilihan peminatan mahasiswa untuk tugas akhir. Jurnal Informatika Darmajaya, 15(2), 1-12..
  26. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. https://doi.org/10.1016/S001 9-9958(65)90241-X
  27. Zhang, Z., Lin, H., Liu, K., Wu, D., Zhang, G., & Lu, J. (2014). A hybrid fuzzy-based personalised recommender system for telecom products/services. Information Sciences, 235, 117–129. https://d oi.org/10.1016/j.ins.2013.07.007
  28. Zhao, X., Zhang, W., & Wang, J. (2021). A fuzzy user preference model for library recommendation systems in smart campuses. IEEE Access, 9, 76812–76824. https://doi.org/10.1109/ACCESS.202 1.3082974
  29. Zolfaghari, S., Shafahi, Y., & Riahifar, N. (2022). A knowledge-based recommender system for academic book selection: A fuzzy approach. Library Hi Tech, 40(5), 1367-1385. https://doi.org/10. 1108/LHT-02-2021-0069