Article Per Year (5 Year)
p-Index From 2020 - 2025
2.693
P-Index
This Author published in this journals
All Journal JIEM (Journal of Islamic Education Management) Educatio Paedagoria : Jurnal Kajian, Penelitian dan Pengembangan Kependidikan Islamic Management: Jurnal Manajemen Pendidikan Islam Gema Wiralodra Wahana Karya Ilmiah Pendidikan Sunan Kalijaga International Journal on Islamic Educational Research Jurnal Sosiohumaniora Kodepena (JSK) JURNAL KEISLAMAN TOFEDU: The Future of Education Journal Jurnal Al Kahfi : Jurnal Pendidikan Agama Islam Journal of Social Work and Science Education Journal of Society and Development Multidiscience: Journal of Multidisciplinary Science
Masduki
IAIN Syekh Nurjati Cirebon
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Social Sciences Other

Published : 18 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Multidiscience: Journal of Multidisciplinary Science

 1 
Analysis of Vocational Education Policy in the Context of Artificial Intelligence Disruption and Its Implications for the Merdeka Curriculum Muis, Rizal Rahmawan; Nadhiroh, Nadhiroh; Yasin, Roby Lafadza; Duryat, Masduki; Suherman, Aris
Multidiscience : Journal of Multidisciplinary Science Vol. 2 No. 2 (2025): June
Publisher : CV. Strata Persada Academia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59631/multidiscience.v2i2.380

Abstract

This study aims to analyze vocational education policy in Indonesia in the context of Artificial Intelligence (AI) disruption and to examine its implications for the implementation of the Merdeka Curriculum. Employing a descriptive qualitative approach and content analysis of policy documents, scholarly publications, and research reports, this study explores the dynamics of vocational education policy in response to the digital transformation driven by AI. The findings reveal that Indonesia's vocational education policy is still in its early stages of integrating AI technologies into curricula and instructional practices. Although the Merdeka Curriculum offers flexibility, it has not yet systematically incorporated AI-related competencies, leaving graduates inadequately prepared for the increasingly digitized labor market. Compared to countries such as Germany and South Korea, Indonesia lags behind in establishing AI-based vocational training through strong industry-education collaboration. To address these challenges, three strategic actions are recommended: revising the curriculum to include AI literacy, strengthening industry partnerships through dual training programs and teacher certification, and investing in equitable digital infrastructure. This study offers both conceptual and practical contributions to the development of adaptive, responsive, and future-oriented vocational education policies in the era of technological disruption.
The Concept of Deep Learning and Its Implementation in Character-Based Learning Rasyad, Muhammad Ghaffar; Suherman, Aris; Duryat, Masduki; Ali, Moh
Multidiscience : Journal of Multidisciplinary Science Vol. 2 No. 2 (2025): June
Publisher : CV. Strata Persada Academia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59631/multidiscience.v2i2.381

Abstract

This study aims to explore the concept and implementation of Deep Learning (DL) in the context of character-based education. Amid the growing urgency of character education in shaping a generation with integrity, advancements in information technology present new opportunities to design more adaptive and contextual learning approaches. DL, as a branch of artificial intelligence, possesses the capability to analyze large-scale digital data and automatically identify behavioral patterns and expressions of character values. This research employs a literature review using a descriptive qualitative approach, analyzing scientific literature from journals and academic publications published between 2015 and 2025. The findings indicate that DL can be utilized to detect learners’ behaviors, emotional expressions, and moral responses across digital media in a contextual manner, thereby supporting formative assessments and data-driven interventions in character education. However, challenges such as algorithm interpretability, data bias, and ethical concerns remain significant. Therefore, the study recommends the application of Explainable AI (XAI) frameworks and multidisciplinary collaboration to ensure that DL is implemented in ethically responsible and pedagogically meaningful ways. These findings offer both theoretical and practical contributions to the development of character learning models aligned with the demands of the digital age.
Co-Authors Akmal, Fauzi Albizar, Albizar Ali Hasan aris suherman, aris Azani , Fajriyan Megawati Aziz Rizki Miftahul Ilmi Darajat, Zulfa Awaliyah Fahmi Arfan Faisal Febrian, St. Ade Moon H. Moch Sholeh Hasan Rahmat Ikramullah Zein Iswandi Iswandi Juniari Tri Trisno Kemal, Selva Noufalia Kristin Warningsih Lulu Alwiyah Moh Ali, Moh Muchsin Muchsin Muh. Aripin Nurmantoro Muhammad Usman Muis, Rizal Rahmawan Nadhiroh, Nadhiroh Nuriyah, Dewi Shinta qomariyah, eva Rahmiati, Ati Rasyad, Muhammad Ghaffar Safana Diva Azjka Savitri Tungga Saddami Sehan Rifky Siregar, Irma Suryani Siti Julaekha Sriwardona, Sriwardona Suhatma Suhatma Suklani Tamsik Udin Yasin, Roby Lafadza