ADAPTIVE AL-QUR’AN MEMORIZATION RECOMMENDATION SYSTEM BASED ON FUZZY LOGIC COGNITIVE MEMORY AND PROFILE MATCHING
DOI:
https://doi.org/10.33480/jitk.v11i3.8048Keywords:
Adaptive Recommender System, Cognitive Modeling, Fuzzy Logic, Multi-Attribute Decision Making (MADM), Mutasyabihat VersesAbstract
Memorizing mutasyabihat verses in the Qur’an is particularly challenging due to similarities in structure, linguistic patterns, and semantic density that place a heavy load on short-term memory. Conventional memorization approaches do not account for individual cognitive differences when dealing with verse complexity. This study proposes an adaptive recommender system based on cognitive modeling to align verse group selection with the user’s memory profile.The system models memory capacity as a multidimensional profile using fuzzy inference derived from three quantitative indicators: continuous memory score, total correct recall, and average response time. This profile is matched with verse group feature vectors through a profile matching approach and a weighted Euclidean distance similarity measure within a Multi-Attribute Decision Making (MADM) framework. Four verse characteristics are considered: thematic (35%), semantic (25%), linguistic (25%), and pattern (15%).An adaptive calibration phase combines 20% of the initial cognitive profile with 80% of actual memorization performance, reflecting the dominance of behavioral evidence over initial assessment. System evaluation employs the Top-N Accuracy method commonly used in recommender systems.Testing with 29 participants resulted in a Top-3 success rate of 66% and an overall Top-N accuracy of 62.07%. These results indicate that cognitive profile–based multidimensional similarity can adaptively match verse complexity to individual memory capacity. This study demonstrates that fuzzy cognitive modeling and profile matching can be effectively implemented in adaptive personalized learning systems to optimize memorization of mutasyabihat verses
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[1] I. Ma’ali, E. Budiharjo, and Z. Abidin, “Psychological Dimension in The Design of Islamic Education Learning Technology Based on Piaget’s Theory,” JSRET (Journal of Scientific, vol. 4, p. 2025.
[2] S. Sirin, B. Metin, and N. Tarhan, “The Effect of Memorizing the Quran on Cognitive Functions,” The Journal of Neurobehavioral Sciences, vol. 8, no. 1, pp. 22–27, Jan. 2021, doi: 10.4103/jnbs.jnbs_42_20.
[3] I. Ishak, “Does Quran Memorization Influence Adolescents’ Intelligence Quotient and Memory Level?: A Cross-Sectional Study in Malaysia,” Makara Journal of Health Research, vol. 25, no. 3, 2021, doi: 10.7454/msk.v25i3.1299.
[4] Hasbi and A. Mardhiah, “Analisis Efektivitas Pembelajaran Tahfidz dalam Meningkatkan Hafalan Al-Qur’an Siswa di Sekolah Dasar Islam Qur’ani Banda Aceh,” Jurnal Pendidikan Tambusai, vol. 8, no. 3, 2024.
[5] D. Adriani, P. Amani, M. A. Putri, Y. Imran, and A. Fauzi, “MENGHAFAL AL-QURAN: TINJAUAN FUNGSI KOGNITIF,” JURNAL PENELITIAN DAN KARYA ILMIAH LEMBAGA PENELITIAN UNIVERSITAS TRISAKTI, pp. 147–151, Jan. 2024, doi: 10.25105/pdk.v9i1.17487.
[6] F. R. Muslim, M. A. Tumiran, and M. Z. H. Zainal Abidin, “Techniques for Memorizing the Quran: A Comparative Study of the Memory System Tendencies of Maahad Integrasi Tahfiz Selangor (MITS) Students,” UMRAN - International Journal of Islamic and Civilizational Studies, vol. 11, no. 2, pp. 37–47, May 2024, doi: 10.11113/umran2024.11n2.647.
[7] A. Permadi and Suyadi, “THE ROLE OF LONG-TERM MEMORY IN QUR’AN LEARNING: COGNITIVE NEUROSCIENCE PERSPECTIVES,” As-Sulthan Journal Education, vol. 2, no. 2, pp. 137–149, 2025, [Online]. Available: https://ojssulthan.com/asje
[8] N. Hasanah, “Strategi Murojaah Huffaz dalam Menjaga Hafalan Al-Qur’an,” 2021. [Online]. Available: https://repository.uin-suska.ac.id/77449
[9] R. Ajriyah, “Metode Murojaah dalam Meningkatkan Kualitas Hafalan Al-Qur’an,” Al-Liqa: Jurnal Ilmu Pendidikan Islam, 2022, [Online]. Available: https://ejournal.stai-tbh.ac.id/al-liqo/article/view/140
[10] A. Muadz, N. Azhar, D. Pradeka, D. Aprianti, and R. Agustini, “Study Program Selection Recommendation System Using the Fuzzy Inference System Mamdani,” 2024.
[11] R. Dian Rasyada, Nurdin, and Fajriana, “Penerapan Metode Profile Matching Analysis pada Sistem Pendukung Keputusan untuk Rekomendasi Program Studi Application of the Profile Matching Analysis Method in Decision Support Systems for Study Program Recommendations,” SISTEMASI: Jurnal Sistem Informasi, vol. 13, no. 1, pp. 83–95, 2024, [Online]. Available: http://sistemasi.ftik.unisi.ac.id
[12] F. J. Schmidig et al., “A visual paired associate learning (vPAL) paradigm to study memory consolidation during sleep,” J. Sleep Res., vol. 33, no. 5, Oct. 2024, doi: 10.1111/jsr.14151.
[13] C. Pettigrew et al., “Computerized paired associate learning performance and imaging biomarkers in older adults without dementia,” Brain Imaging Behav., vol. 16, no. 2, pp. 921–929, Apr. 2022, doi: 10.1007/s11682-021-00583-9.
[14] C.-J. and S. K.-W. Liu Cheng-Li and Chuang, “A Fuzzy System for Forecasting Mild Cognitive Impairment,” in Intelligent and Fuzzy Systems, I. U. and O. B. and C. S. and C. O. S. and T. A. Ç. Kahraman Cengiz and Sari, Ed., Cham: Springer Nature Switzerland, 2023, pp. 717–724.
[15] J. H. Lo, H. P. Huang, and S. C. Sung, “Working Memory Ability Evaluation Based on Fuzzy Support Vector Regression,” Sensors, vol. 23, no. 19, Oct. 2023, doi: 10.3390/s23198246.
[16] G. A.Radvansky, A. C.Doolen, K. A.Pettijohn, and M. RItchey, “Supplemental Material for A New Look at Memory Retention and Forgetting,” J. Exp. Psychol. Learn. Mem. Cogn., 2022, doi: 10.1037/xlm0001110.supp.
[17] G. A.Radvansky, Human Memory, vol. 4. 2021.
[18] M. Koizumi and M. Kojima, “Syntactic development and verbal short-term memory of children with autism spectrum disorders having intellectual disabilities and children with down syndrome,” Autism Dev. Lang. Impair., vol. 7, 2022, doi: 10.1177/23969415221109690.
[19] G. García-Lluch, A. Muedra-Moreno, M. García-Zamora, B. Gómez, R. Sánchez-Roy, and L. Moreno, “Selecting a Brief Cognitive Screening Test Based on Patient Profile: It Is Never Too Early to Start,” J. Clin. Med., vol. 13, no. 19, p. 6009, Oct. 2024, doi: 10.3390/JCM13196009/S1.
[20] H. M. Jungeblut, D. Hagemann, C. Löffler, and A. L. Schubert, “An Investigation of the Slope Parameters of Reaction Times and P3 Latencies in the Sternberg Memory Scanning Task - A Fixed-Links Model Approach,” J. Cogn., vol. 4, no. 1, pp. 1–19, Apr. 2021, doi: 10.5334/JOC.158.
[21] J. Klabes, S. Babilon, B. Zandi, and T. Q. Khanh, “The sternberg paradigm: Correcting encoding latencies in visual and auditory test designs,” Vision (Switzerland), vol. 5, no. 2, 2021, doi: 10.3390/vision5020021.
[22] K. A. Abd Aziz, M. F. I. Mohd Idris, W. S. Wan Daud, and A. M. Tajaludin, “Assessment of Qurâ€TMan Reciting Competitions Using the Fuzzy Evaluation Method,” Journal of Computing Research and Innovation, vol. 7, no. 2, pp. 66–73, Sep. 2022, doi: 10.24191/jcrinn.v7i2.287.
[23] M. Maulidah, N. Rahmadani, and D. Prasetyo, “Sistem Pendukung Keputusan Kelayakan Usaha UMKM Menggunakan Metode Profile Matching,” Jurnal Sains dan Informatika, vol. 9, no. 2, pp. 122–130, 2023, [Online]. Available: https://www.jurnal.polgan.ac.id/index.php/sinkron/article/view/13638
[24] A. Rohim, “Pengembangan SMART: Sistem Penilaian Media Pembelajaran Berbasis Teknologi Fuzzy Mamdani di Excel,” WAHANA PEDAGOGIKA, vol. 7, no. 1, 2025.
[25] H. D. Adha, L. E. Zen, H. Hanim, H. Prasetyo, and Novi, “Pengimplementasian Metode Simple Multi Attribute Rating Technique pada Sistem Pendukung Keputusan: Literatur Review,” Jurnal Informatika Ekonomi Bisnis, pp. 680–683, Sep. 2025, doi: 10.37034/infeb.v7i3.1267.
[26] B. He, X. He, R. Zhang, Y. Zhang, R. Tang, and C. Ma, “Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System,” in International Conference on Information and Knowledge Management, Proceedings, Association for Computing Machinery, Oct. 2023, pp. 741–750. doi: 10.1145/3583780.3615135.
[27] Y. Qu, T. Chen, X. Zhao, L. Cui, K. Zheng, and H. Yin, “Continuous Input Embedding Size Search For Recommender Systems,” in SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Association for Computing Machinery, Inc, Jul. 2023, pp. 708–717. doi: 10.1145/3539618.3591653.
[28] C. Ma, L. Ma, Y. Zhang, R. Tang, X. Liu, and M. Coates, “Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation,” Jan. 2021, [Online]. Available: http://arxiv.org/abs/2101.04849
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