Dataset Development for Quran Memorizers: A Step Towards Data-Driven Personalized Learning Path

Authors

Miftah Farid Adiwisastra
Universitas Bina Sarana Informatika image/svg+xml
Irfan Darmawan
Telkom University image/svg+xml
Dade Nurjanah
Telkom University image/svg+xml
DOI: https://doi.org/10.52549/ijeei.v14i1.7343
Download PDF
Published: 31-03-2026 • Issue: Vol. 14 No. 1: March 2026 • Section: Regular paper

Abstract

Advances in artificial intelligence and machine learning have created new opportunities for improving Quran memorization methods. A key requirement for such innovation is the availability of structured and representative datasets specifically designed for Quran memorizers. This study presents the design and development of a dataset that captures demographic characteristics, daily learning behavior, and temporal memorization patterns from 350 students across three Islamic boarding schools in Indonesia. Through preprocessing stages, including normalization, discretization, and feature engineering, the dataset was prepared for Hidden Markov Model (HMM)-based analysis. Experimental results show that the model achieved an accuracy of 30.57%, precision of 71.46%, recall of 30.57%, and F1-score of 37.70% in predicting memorization states. These findings indicate that the proposed dataset provides a useful foundation for modeling memorization progress and supporting adaptive learning path recommendations. However, the study is limited by the relatively small dataset and the modest predictive performance of the initial HMM model. Overall, this work provides an important first step toward building an intelligent and personalized data-driven Quran memorization system.
Keywords: Quran Memorization; Dataset Development; Personalized Learning; Machine Learning; Artificial Intelligence

References

K. Pan and R. Sen, “Navigating the evolving landscape of personalized learning in aied: Recent trends and innovations,†Explainable AI for Education: Recent Trends and Challenges, pp. 169–192, 2024.

M. Bendahmane, B. El Falaki, and M. Benattou, “Toward a personalized learning path through a services- oriented approach,†International Journal of Emerging Technologies in Learning (Online), vol. 14, no. 15, p. 52, 2019.

A. Bhutoria, “Personalized education and artificial intelligence in the united states, china, and india: A systematic review using a human-in-the-loop model,†Computers and Education: Artificial Intelligence, vol. 3, p. 100068, 2022.

K.-J. Laak and J. Aru, “Ai and personalized learning: bridging the gap with modern educational goals,†arXiv preprint arXiv:2404.02798, 2024.

C. Song, S.-Y. Shin, and K.-S. Shin, “Implementing the dynamic feedback-driven learning optimization framework: a machine learning approach to personalize educational pathways,†Applied Sciences, vol. 14, no. 2, p. 916, 2024.

B. Curum and K. K. Khedo, “Cognitive load management in mobile learning systems: principles and theories,†Journal of Computers in Education, vol. 8, no. 1, pp. 109–136, 2021.

D. Kapadiya, C. Shekhawat, and P. Sharma, “A study on largescale applications of big data in modern era,†in Proceedings of the 5th International Conference on Information Management and Machine Intelligence, 2023, pp. 1–6.

F. Alvindo, N. A. Setiawan, and H. A. Nugroho, “Al-quran recitation or sound processing analysis: A systematic literature review on methods,†in 2024 International Conference on Electrical Engineering and Computer Science (ICECOS). IEEE, 2024, pp. 54–58.

A. Da’u and N. Salim, “Recommendation system based on deep learning methods: a systematic review and new directions,†Artificial Intelligence Review, vol. 53, no. 4, pp. 2709–2748, 2020.

R. J. K. Almahmood and A. Tekerek, “Issues and solutions in deep learning-enabled recommendation systems within the ecommerce field,†Applied Sciences, vol. 12, no. 21, p. 11256, 2022.

B. Ramzan, I. S. Bajwa, N. Jamil, R. U. Amin, S. Ramzan, F. Mirza, and N. Sarwar, “An intelligent data analysis for recommendation systems using machine learning,†Scientific Programming, vol. 2019, no. 1, p. 5941096, 2019.

E. Hasan, M. Rahman, C. Ding, J. Huang, and S. Raza, “Based recommender systems: a survey of approaches, challenges and future perspectives,†ACM Computing Surveys, 2024.

N. M. Mustafa, Z. Mohd Zaki, K. A. Mohamad, M. Basri, and S. Ariffin, “Development and alpha testing of ezhifz application: Al-quran memorization tool,†Advances in Human-Computer Interaction, vol. 2021, no. 1, p. 5567001, 2021.

M. G. B. Musa, M. H. N. B. Yusop, M. M. B. M. Sopee, and N. A. M. Ali, “I-tasmik mobile platform– enabling tahfiz student to memorize al-quran independently,†in 2018 International Conference on Infor- mation and Communication Technology for the Muslim World (ICT4M). IEEE, 2018, pp. 24–29.

R. A. Rajagede, Y. H. Hisyam, M. I. A. Yulianto, F. A. Ridwanto, A. T. Putranto, and M. R. F. P. Danar, “Personalized al-quran memorization testing system using group decision support system,†Aceh Interna- tional Journal of Science and Technology, vol. 10, no. 3, pp. 171–181, 2021.

D. Purbohadi, B. R. Rahmawati, and H. Setiyawan, “Development of qur’an memorization learning model based on mobile learning,†in Journal of Physics: Conference Series, vol. 1381, no. 1. IOP Publishing, 2019, p. 012029.

N. Senan, W. A. W. Ab Aziz, M. F. Othman, and S. Suparjoh, “Embedding repetition (takrir) technique in developing al-quran memorizing mobile application for autism children,†in MATEC Web of Conferences, vol. 135. EDP Sciences, 2017, p. 00076.

I. K. Tantawi, M. A. Abushariah, and B. H. Hammo, “A deep learning approach for automatic speech recognition of the holy qur’¯an recitations,†International Journal of Speech Technology, vol. 24, no. 4, pp. 1017–1032, 2021.

Y. S. Alsahafi and M. Asad, “Empirical study on mispronunciation detection for tajweed rules during quran recitation,†in 2024 6th International Conference on Computing and Informatics (ICCI). IEEE, 2024, pp. 39–45.

S. M. Hussain, M. N. Jilani, M. U. Haq, and M. S. Shaikh, “A machine learning approach to arabic phoneme classification through ensemble techniques,†in 2024 5th International Conference on Advance- ments in Computational Sciences (ICACS). IEEE, 2024, pp. 1–7.

M. K. K. Alsbou and R. A. I. Alsaraireh, “Data-driven decision-making in education: Leveraging ai for school improvement,†in 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS), vol. 1. IEEE, 2024, pp. 1–6.

L. Tetzlaff, F. Schmiedek, and G. Brod, “Developing personalized education: A dynamic framework,†Educational Psychology Review, vol. 33, pp. 863–882, 2021.

R. K. Yekollu, T. Bhimraj Ghuge, S. Sunil Biradar, S. V. Haldikar, and O. Farook Mohideen Abdul Kader, “Ai-driven personalized learning paths: Enhancing education through adaptive systems,†in International Conference on Smart data intelligence. Springer, 2024, pp. 507–517.

I. Kabashkin, B. Miˇsn¸evs, and O. Zervina, “Ai-driven and ontology-based framework for personalized learning pathways in education,†in International Conference on Reliability and Statistics in Transporta- tion and Communication. Springer, 2024, pp. 494–506.

Y. Luo, “Personalized learning paths based on big data analysis in digital english teaching,†in 2024 3rd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS). IEEE, 2024, pp. 734–738.

Y. Guo and Y. Li, “Exploration of personalized teaching paths in english education based on big data anal- ysis,†Journal of Computational Methods in Sciences and Engineering, p. 14727978251322283, 2025.

C. G. Demartini, L. Sciascia, A. Bosso, and F. Manuri, “Artificial intelligence bringing improvements to adaptive learning in education: A case study,†Sustainability, vol. 16, no. 3, p. 1347, 2024.

T. Kabudi, I. Pappas, and D. H. Olsen, “Ai-enabled adaptive learning systems: A systematic mapping of the literature,†Computers and education: Artificial intelligence, vol. 2, p. 100017, 2021.

X. Wang, R. uang, M. Sommer, B. Pei, P. Shidfar, M. S. Rehman, A. D. Ritzhaupt, and F. Martin, “The ef- ficacy of artificial intelligence-enabled adaptive learning systems from 2010 to 2022 on learner outcomes: a meta-analysis,†Journal of Educational Computing Research, vol. 62, no. 6, pp. 1568–1603, 2024.

L. Lim, S. H. Lim, and W. Y. R. Lim, “A rasch analysis of students’ academic motivation toward mathe- matics in an adaptive learning system,†Behavioral Sciences, vol. 12, no. 7, p. 244, 2022.

Author Biographies

Miftah Farid Adiwisastra
Information System

How to Cite

[1]
M. F. Adiwisastra, I. Darmawan, and D. Nurjanah, “Dataset Development for Quran Memorizers: A Step Towards Data-Driven Personalized Learning Path”, IJEEI, vol. 14, no. 1, pp. 264–280, Mar. 2026, doi: 10.52549/ijeei.v14i1.7343.