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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization JURIKOM (Jurnal Riset Komputer) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Bimas Islam Southeast Asia Journal on Open and Distance Learning
Diena Rauda Ramdania
UIN Sunan Gunung Djati Bandung
Religion Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Mathematics Social Sciences Other

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Exploring Classification Algorithms for Detecting Learning Loss in Islamic Religious Education: A Comparative Study Sapdi, Rohmat Mulyana; Maylawati, Dian Sa'adillah; Ramdania, Diena Rauda; Budiman, Ichsan; Al-Amin, Muhammad Insan; Fuadi, Mi'raj
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1823

Abstract

This study investigates the detection of learning loss in Islamic religious education subjects in Indonesia. Focusing on the effectiveness of multiple classification algorithms, the research assesses learning loss across literacy, numeracy, writing, and science domains. While education traditionally involves knowledge transmission, it also seeks to instill values. Given Indonesia's predominantly Islamic demographic, Islamic Religious Education (IRE) is pivotal in disseminating moral and cultural values, encompassing teachings from the Koran, Hadith, Aqedah, morality, Fiqh, and Islamic history. The study's central aim is to discern learning loss in IRE in Islamic schools, utilizing the Gradient Boosting Classifier as its primary analytical tool. Various classification algorithms, including the Cat Boost Classifier, Light Gradient Boosting Machine, Extreme Gradient Boosting, and others, were tested. The study engaged a sample of 38,326 Islamic Elementary school students, 29,350 Islamic Junior High school students, and 13,474 Islamic High school students across Indonesia. The findings revealed that the Light Gradient Boosting Machine was the most effective model for Islamic Elementary and High school data, while the Cat Boost Classifier excelled for Islamic Junior High school data. These results highlight the extent of learning loss in IRE and offer invaluable perspectives for education stakeholders. Future studies are encouraged to further explore the root causes of this learning loss and devise specific interventions to tackle these issues effectively.
Analysis of the Utilization of Artificial Intelligence Technology in Learning in Indonesia Learning Alignment Diena Rauda Ramdania; Handi Pradana; Fazhar Restu Fauzi
Southeast Asian Journal on Open and Distance Learning Vol. 1 No. 02 (2023): Capturing the Future of Education with AI-Driven Innovations in Online Learnin
Publisher : SEAMEO SEAMOLEC

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Abstract

In the contemporary digital era, integrating Artificial Intelligence (AI) into educational practices is vital for enhancing educational effectiveness. This study assesses the readiness of educational institutions in Indonesia, and to some extent in regions like Taipei, Thailand, Malaysia, and Lao PDR, to adopt AI in their learning processes. Employing an Exploratory Research approach, the study involved a Likert scale-based survey with 250 respondents, including educators and students from various educational levels, predominantly from Vocational and Secondary High Schools, as well as Higher Education Institutions. Findings indicate a moderate level of preparedness among educators and students for AI integration in education. Educators exhibit basic digital skills but show significant variation in AI-related competencies, highlighting the need for enhanced professional development in AI learning strategies. Students display moderate access to AI-supportive technology and a foundational understanding of AI applications and digital ethics, suggesting a necessity for improved digital literacy training and resources. The study underscores the importance of structured AI-specific training and collaboration among educational stakeholders to optimize AI's potential in education.
Co-Authors Aaz Muhammad Hafidz Azis Agung Wahana Agus Sulaeman Aldy Rialdy Atmadja Ayu Puji Rahayu Busro, B. Cepy Slamet Deden Suparman, Deden Dhanis Al Ghifari Fahmi Faturahman Nul Hakim Faiz M. Kaffah Fazhar Restu Fauzi Ferry Feirizal Fitri Diani Gina Giftia Azmiana Delilah Handi Pradana Ichsan Budiman Irfan Safrudin Khaerul Manaf Kokoy Siti Komariah Maisevli Harika Maisevli Harika Maylawati, Dian Sa'adillah Mi’raj Fuadi Mohamad Irfan Muhammad Ali Ramdhani Muhammad Fasya Adzkia Muhammad Insan Al-Amin Rifaldo Sukma Hidayat Rohmat Mulyana Sapdi Safira Oktarini Salma Nuralisa Habsah Sandi Rahmadika Sandi Rahmadika Taufik, Ichsan Wildan Budiawan Zulfikar Wisnu Uriawan, Wisnu Yana Aditia Gerhana Yana Aditia Gerhana, Yana Aditia Yudi Widhiyasana