ONTOLOGICAL COMPARISON OF THE INTERPRETATION OF ISRA' AND MI'RAJ USING TOPIC MODELING BERTOPIC
DOI:
https://doi.org/10.31102/alulum.13.1.2026.13-24Keywords:
ONTOLOGICAL COMPARISON, INTERPRETATION OF ISRA' AND MI'RAJ, TOPIC MODELINGAbstract
The Isra' Mi'raj event is a central theme in the Islamic tradition that contains physical, metaphysical, and transcendental dimensions. The diversity of interpretations regarding the ontological nature of the journey of the Prophet Muhammad PBUH is reflected in classical and contemporary works of interpretation. This study aims to map and compare the ontological interpretation pattern of Isra'–Mi'raj with the text mining approach using BERTopic, a transformer-based topic modeling technique that is able to identify latent themes computationally. The research corpus consists of tafsir texts from various periods, including Tafsir Ibn Katsir, Jalalain, al-Qurthubi, Tafsir al-Mishbah, and Tafsir of the Ministry of Religion of the Republic of Indonesia. The research stages include text preprocessing, semantic representation extraction, topic formation, and visualization of proximity and structure between topics through intertopic distance maps, topic word scores, and similarity matrix. The results of the study show that the ontological interpretation of Isra'–Mi'raj is grouped into three main categories: (1) physical-bodily ontology, (2) spiritual-metaphysical ontology, and (3) transcendental-cosmological ontology. Comparative analysis shows that classical interpretations tend to be based on literal-physical interpretations, while contemporary interpretations are more integrative with rational, symbolic, and metaphysical approaches. This study confirms that BERTopic is effective in systematically uncovering ontological meaning patterns and making a methodological contribution to the development of artificial intelligence-based interpretation studies.
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References
Akinwole, A. THE SCIENCE AND INFORMATION ORGANIZATION. www.ijacsa.thesai.org
Alnagi, E., Ghnemat, R., & Abu Al-Haija, Q. (2025). Boosting Arabic text classification using hybrid deep learning approach. Discover Applied Sciences, 7(6), 540. https://doi.org/10.1007/s42452-025-07025-x
Badry, M., Hassanin, M., Chandio, A., & Moustafa, N. (2021). Quranic script optical text recognition using deep learning in IoT systems. Computers, Materials, & Continua, 68(2), 1847. https://doi.org/10.32604/cmc.2021.015489
Bamoki, M., Wady, S. H., & Badawi, S. (2025). Holy Quran Kurdish Sorani translation dataset for language modelling. Data in Brief, 60, 111533. https://doi.org/10.1016/j.dib.2025.111533
Elfaik, H. (2021). Combining context-aware embeddings and an attentional deep learning model for Arabic affect analysis on Twitter. Ieee Access, 9, 111214-111230. https://doi.org/10.1109/ACCESS.2021.3102087
Metwally, A. A., & Bin-Hady, W. R. A. (2025). Exploring human vs. AI-powered translation to metonymic expressions: A case study of the Holy Quran. Social Sciences & Humanities Open, 12, 101615. https://doi.org/10.1016/j.ssaho.2025.101615
Mohamed, E. H., & Shokry, E. M. (2022). QSST: A Quranic Semantic Search Tool based on word embedding. Journal of King Saud University-Computer and Information Sciences, 34(3), 934-945. https://doi.org/10.1016/j.jksuci.2020.01.004
Mohd, M., Qamar, F., Al-Sheikh, I., & Salah, R. (2021). Quranic optical text recognition using deep learning models. Ieee Access, 9, 38318-38330. https://doi.org/10.1109/ACCESS.2021.3064019
Mosa, M. A. (2025). Synergizing structure and semantics: a knowledge graph-transformer framework for narrator disambiguation in hadith networks. Digital Scholarship in the Humanities, 40(4), 1085-1100. https://doi.org/10.1093/llc/fqaf088
Shahriar, S., & Tariq, U. (2021). Classifying maqams of Qur’anic recitations using deep learning. Ieee Access, 9, 117271-117281. https://doi.org/10.1109/ACCESS.2021.3098415
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