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All Journal JOIV : International Journal on Informatics Visualization JURNAL DINAMIKA VOKASIONAL TEKNIK MESIN SYAMIL: Jurnal Pendidikan Agama Islam (Journal of Islamic Education) Data Science: Journal of Computing and Applied Informatics JISIP: Jurnal Ilmu Sosial dan Pendidikan JUPE : Jurnal Pendidikan Mandala Journal of Computer Networks, Architecture and High Performance Computing Jounal of Education and Teaching (JET) SYAMIL: JURNAL PENDIDIKAN AGAMA ISLAM (JOURNAL OF ISLAMIC EDUCATION) Jurnal Pengabdian Masyarakat Bangsa Jurnal Elektronika dan Teknik Informatika Terapan Journal Of Engineering Sciences (Improsci)
Fahmy Ferdian Dalimarta
Universitas Muhammadiyah Tegal, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Transforming Islamic and Moral Education with Generative AI: A Statistical Systematic Review Budiyanto Budiyanto; Ali Said Al Matari; Adiyono Adiyono; Fahmy Ferdian Dalimarta
SYAMIL: Journal of Islamic Education Vol. 13 No. 3 (2025): SYAMIL: Journal of Islamic Education
Publisher : Pascasarjana Universitas Islam Negeri Sultan Aji Muhammad Idris Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21093/sy.v13i3.11935

Abstract

The integration of artificial intelligence is reshaping global education. This study systematically investigates the role of semi-supervised generative AI in transforming Islamic and moral education. This study aims to systematically investigate the role of semi-supervised generative artificial intelligence (AI) in transforming Islamic and moral education through a PRISMA-guided statistical systematic literature review. The increasingly widespread integration of AI tools such as ChatGPT, Gemini, DeepSeek, Agnes AI, and Cici has reshaped pedagogical practices, yet few studies have quantitatively examined their impact in faith-based education. A key issue addressed is the limited empirical understanding of how semi-supervised learning models mediate between human-guided moral instruction and AI-driven autonomous reasoning. Data were extracted from 38 peer-reviewed publications (2020–2025) across major databases and analyzed using statistical synthesis and meta-analysis. The results indicate a moderate positive effect size (d = 0.56) for generative AI in enhancing student engagement, critical thinking, and ethical reasoning in Islamic learning contexts. Tools such as ChatGPT and Gemini demonstrated the strongest pedagogical outcomes, while Agnes AI and Cici demonstrated unexplored potential. This study concludes that semi-supervised generative AI offers significant opportunities for pedagogical innovation and improved moral reasoning, although ethical supervision and the development of local AI models remain critical for sustainable implementation.
Transforming Islamic and Moral Education with Generative AI: A Statistical Systematic Review Budiyanto Budiyanto; Ali Said Al Matari; Adiyono Adiyono; Fahmy Ferdian Dalimarta
SYAMIL: Journal of Islamic Education Vol. 13 No. 3 (2025): SYAMIL: Journal of Islamic Education
Publisher : Pascasarjana Universitas Islam Negeri Sultan Aji Muhammad Idris Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21093/sy.v13i3.11935

Abstract

The integration of artificial intelligence is reshaping global education. This study systematically investigates the role of semi-supervised generative AI in transforming Islamic and moral education. This study aims to systematically investigate the role of semi-supervised generative artificial intelligence (AI) in transforming Islamic and moral education through a PRISMA-guided statistical systematic literature review. The increasingly widespread integration of AI tools such as ChatGPT, Gemini, DeepSeek, Agnes AI, and Cici has reshaped pedagogical practices, yet few studies have quantitatively examined their impact in faith-based education. A key issue addressed is the limited empirical understanding of how semi-supervised learning models mediate between human-guided moral instruction and AI-driven autonomous reasoning. Data were extracted from 38 peer-reviewed publications (2020–2025) across major databases and analyzed using statistical synthesis and meta-analysis. The results indicate a moderate positive effect size (d = 0.56) for generative AI in enhancing student engagement, critical thinking, and ethical reasoning in Islamic learning contexts. Tools such as ChatGPT and Gemini demonstrated the strongest pedagogical outcomes, while Agnes AI and Cici demonstrated unexplored potential. This study concludes that semi-supervised generative AI offers significant opportunities for pedagogical innovation and improved moral reasoning, although ethical supervision and the development of local AI models remain critical for sustainable implementation.
Monitoring Ketinggian Air Sungai menggunakan Regresi Linear Sederhana Berbasis Internet of Things (IoT) di Sungai Gung Desa Kendalserut Firman Ardy Prasetyo; Fahmy Ferdian Dalimarta; Sonhaji Sonhaji
Jurnal Elektronika dan Teknik Informatika Terapan Vol. 4 No. 3 (2026): September: Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK )
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jentik.v4i3.1519

Abstract

Monitoring the water level of the Gung River in Kendalserut Village is still conducted manually using wooden poles, making the process less effective, requiring human involvement, and potentially producing inaccurate data. This condition makes it difficult for the community to obtain timely information, particularly when increasing discharge may cause flooding and damage agricultural land. This study aims to design and implement an Internet of Things (IoT)-based water level monitoring system using Simple Linear Regression to analyze the relationship between water level and river discharge. A quantitative approach was employed through observation, interviews, documentation, and data collection using an ultrasonic sensor. The system was developed with an ESP32 microcontroller with an ultrasonic sensor to measure water levels. Data were transmitted via the internet and processed using Simple Linear Regression, with water level as the independent variable and river discharge as the dependent variable. Monitoring results were delivered through WhatsApp notifications. The results show that the system can automatically monitor river water levels in real time and provide more accurate discharge information. The system is expected to support effective flood risk mitigation.
Co-Authors Adiyono Adiyono Adiyono Adiyono Ali Said Al Matari Ali Said Al Matari Andono, Pulung N. Budiyanto Budiyanto Didi Kusaeri Doni Setiawan Faoziah, Nina Faoziyah, Nina Firman Ardy Prasetyo Jaelani Jaelani Muzakki Mahdi Nugroho Susanto Pratama, Aziz Yulianto Pulung Nurtantio Andono Randi Dwi Wibisono Soeleman, Moch. A. Soeleman, Moch. Arief Sonhaji Sonhaji Sulistyo, Adi Zainal A. Hasibuan Zainal Arifin Hasibuan