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TheRêst: An Artificial Intelligence-Based Application for Digital Transformation of Qur’anic Understanding through Adaptive Tafsir and Thematic Sermons

Journal of Advances in Information Systems and Technology · Vol. 7 No. 1 (2025)

DOI
10.15294/jaist.v7i1.33906
ISSN
2715-999X, 2714-9714
Terbit
2025-10-06
Penerbit
Universitas Negeri Semarang
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0/

Abstrak

The advancement of digital technology and internet penetration has opened wide opportunities for the development of Islamic educational applications. However, most existing Qur’an applications remain static and function merely as digitized texts without adaptive features. This study introduces TheRêst, an AI-based mobile application designed to enhance Qur’anic understanding through adaptive tafsir summarization, thematic sermon generation, and contextual sermon analysis. The development followed the Agile methodology, allowing iterative design, development, and testing processes. Key features were built using Natural Language Processing (NLP) and evaluated through black-box testing and usability testing with the System Usability Scale (SUS). Functional testing confirmed that core features—such as tafsir summarization and sermon generation—performed as expected, although challenges remained in response time and long-text accuracy. Usability testing involved 20 participants, including preachers, students of Islamic studies, and general Muslim users. Results showed an average SUS score of 79.25, categorized as “Excellent,” indicating strong user acceptance and satisfaction. Qualitative feedback highlighted the clarity of the interface and the usefulness of AI-generated content, while also noting the need for faster processing. This research contributes to the field of digital Islamic education by demonstrating the feasibility of integrating AI into Qur’an applications, thus providing innovative solutions for preachers and learners. Future work will focus on refining AI models, improving performance, and adding collaborative features. Overall, TheRêst presents a significant step toward creating adaptive, user-centered digital tools that support a deeper and more contextual engagement with the Qur’an.

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  • State University of Semarang
  • Tun Hussein Onn University of Malaysia
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Rekan penulisOrganisasiArtikel bersama
Bambang Eko SusiloState University of Semarang1
Trida Ridho FarizState University of Semarang1
Sri SukaesihState University of Semarang1
Nuni WidiartiState University of Semarang1
Siti WahyuniAfiliasi tidak diketahui1
Aditya MariantiAfiliasi tidak diketahui1
Virgania SariAfiliasi tidak diketahui1
Nafa FajriatiState University of Semarang1
Rizka Nur PratamaAfiliasi tidak diketahui1
Much Aziz MuslimTun Hussein Onn University of Malaysia1
Maylinna Rahayu NingsihAfiliasi tidak diketahui1
Yopi Julia NurriskiAfiliasi tidak diketahui1
Fathimah Az Zahra SanjaniAfiliasi tidak diketahui1
Jumanto UnjungAfiliasi tidak diketahui1
Wiyanda PuspitaAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/jaist/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 6 Nov 2026.
Metadata asli (oai_dc)
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">TheRêst: An Artificial Intelligence-Based Application for Digital Transformation of Qur’anic Understanding through Adaptive Tafsir and Thematic Sermons</dc:title>
	<dc:creator xml:lang="en">Utama, Reiki Aziz Yoga </dc:creator>
	<dc:creator xml:lang="en">Ilham, Ahmad Bagas Aditya</dc:creator>
	<dc:creator xml:lang="en">Hakim, M. Faris Al</dc:creator>
	<dc:subject xml:lang="en">Qur’an</dc:subject>
	<dc:subject xml:lang="en">Artificial Intelligence </dc:subject>
	<dc:subject xml:lang="en">System Usability Scale  </dc:subject>
	<dc:subject xml:lang="en">Sermon automation</dc:subject>
	<dc:subject xml:lang="en">tafsir</dc:subject>
	<dc:description xml:lang="en">The advancement of digital technology and internet penetration has opened wide opportunities for the development of Islamic educational applications. However, most existing Qur’an applications remain static and function merely as digitized texts without adaptive features. This study introduces TheRêst, an AI-based mobile application designed to enhance Qur’anic understanding through adaptive tafsir summarization, thematic sermon generation, and contextual sermon analysis. The development followed the Agile methodology, allowing iterative design, development, and testing processes. Key features were built using Natural Language Processing (NLP) and evaluated through black-box testing and usability testing with the System Usability Scale (SUS). Functional testing confirmed that core features—such as tafsir summarization and sermon generation—performed as expected, although challenges remained in response time and long-text accuracy. Usability testing involved 20 participants, including preachers, students of Islamic studies, and general Muslim users. Results showed an average SUS score of 79.25, categorized as “Excellent,” indicating strong user acceptance and satisfaction. Qualitative feedback highlighted the clarity of the interface and the usefulness of AI-generated content, while also noting the need for faster processing. This research contributes to the field of digital Islamic education by demonstrating the feasibility of integrating AI into Qur’an applications, thus providing innovative solutions for preachers and learners. Future work will focus on refining AI models, improving performance, and adding collaborative features. Overall, TheRêst presents a significant step toward creating adaptive, user-centered digital tools that support a deeper and more contextual engagement with the Qur’an.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2025-10-06</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/jaist/article/view/33906</dc:identifier>
	<dc:identifier>10.15294/jaist.v7i1.33906</dc:identifier>
	<dc:source xml:lang="en">Journal of Advances in Information Systems and Technology; Vol. 7 No. 1 (2025): April</dc:source>
	<dc:source>2715-999X</dc:source>
	<dc:source>2714-9714</dc:source>
	<dc:source>10.15294/jaist.v7i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/jaist/article/view/33906/6317</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Journal of Advances in Information Systems and Technology</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by-sa/4.0/</dc:rights>
</oai_dc:dc>