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Daengku: Journal of Humanities and Social Sciences Innovation
Published by PT. Mattawang Mediatama Solution
ISSN : -     EISSN : 27756165     DOI : https://doi.org/10.35877/454RI.daengkuv1i1
Core Subject : Humanities, Education, Social,
Humanities Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Other
The Daengku seeks to publish high-quality research papers, review articles, and book reviews that make a contribution to knowledge through the application and development of theories, new data exploration, and/or scientific analysis of salient policy issues. The Scope of the Daengku includes the following areas: Social Sciences: Anthropology, Asian Studies, Communication, Demography, Development, Gender Studies, Government & Public Policy, Human Ecology, International Relations, Media Studies, Peace and Conflict, Political Science, Science, Technology & Society, Sociology. Humanities: Cultural Studies, Education, History, Human Geography, Linguistics, Philosophy, Religion.
Arjuna Subject : Umum - Umum
Articles 2 Documents
 1 
Search results for , issue "Vol. 6 No. 2 (2026)" : 2 Documents clear
Integration of the Al-Qur'an and Science to Improve the Intellectual Intelligence of Graduate Quality Aziz, Baihaqi; Maskuri, M; Hakim, Dian Mohammad
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 6 No. 2 (2026)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku4830

Abstract

In the era of globalization, science is dominated by the West, while the contribution of Muslims is often considered minimal. SMA Trensains Tebuireng 2 Jombang integrates the Qur’an and science into the curriculum to improve students’ religiosity and intellectual intelligence. This study aims to examine the verses of the Qur’an, the process, and the science integration model to enhance graduates’ quality. With a qualitative phenomenological approach, data were obtained through observation, interviews, and documentation. The results show integration based on verses such as QS. Al-Syu’ara: 4, QS. Al-Rum: 25, and QS. Al-Insan: 17, which discusses creation and natural phenomena. The process involves a thematic curriculum, an approach to scientific interpretation, and discussion-based learning and presentations. Supporting factors include laboratories, libraries, and institutional support, although constrained by limited human resources and media. This integration model is efficacious in improving intellectual intelligence and religiosity, producing superior graduates based on Islamic values.
Artificial Intelligence–Driven Learning Analytics for Enhancing Student Engagement and Academic Performance in Digital Learning Environments Pratama, Dendi; Ciptaningsih, Eka Maya S.S.; Ramadiani, Ramadiani; Fawaid, Achmad; Firdaus, Winci; Sudarsono, Bambang
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 6 No. 2 (2026)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku4835

Abstract

The quick development of digital learning ecosystems after educational reform in the post-pandemic era requires an increase in intelligent monitoring systems that assess student engagement and predict academic performance. Traditional learning assessment techniques frequently have flaws when detecting early disengagement signals and initiating corrective actions for at-risk students. This research proposes an Artificial Intelligence (AI)-Driven Learning Analytics method that aims to improve student engagement monitoring and academic performance prediction in digital learning environments. A fabricated LMS-based educational dataset was used, which includes behavior analysis, engagement factors, academic factors, interaction factors, and temporal learning behavior obtained from LMSs like Moodle, Google Classroom, and Canvas. Several machine learning models, including Random Forest, XGBoost, Support Vector Machine, Artificial Neural Network, and Long Short-Term Memory (LSTM), were tested. The results revealed that the LSTM model had the best performance with an accuracy rate of 95% and a ROC-AUC value of 0.98, highlighting the importance of temporal learning behavior in educational prediction systems. Some of the essential engagement factors found to be most effective were assignment submission, quiz score, inactivity period, session length, and login number. The findings make a theoretical contribution to Artificial Intelligence in Education and Learning Analytics by combining multidimensional engagement analysis, temporal behavior modeling, and explainable AI into a unified framework. In practice, the suggested framework can aid adaptive learning, early warning, individualized intervention, and evidence-based education decisions in intelligent digital learning ecosystems.

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