Article Per Year (5 Year)
p-Index From 2021 - 2026
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All Journal Journal of Deep Learning
Fajar Riza Ul Haq
Faculty of Social Science, Universitas Muhammadiyah Sukabumi
Education Languange, Linguistic, Communication & Media Social Sciences

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Integrating Technical Skills, Soft Skills, and Digital Technology in Culinary Vocational Education: A Deep Learning Model for Industry 5.0 Herlina Tri Wulandari; Heri Susanto; Harun Joko Prayitno; Indri; Bayu Wardhana; Fajar Riza Ul Haq
Journal of Deep Learning Vol. 2, No. 1, June 2026
Publisher : Universitas Muhammadiyah Surakarta in Collaboration with Kemdikdasmen RI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/jdl.v2i1.12927

Abstract

The emergence of Industry 5.0 requires vocational education to integrate technical competencies with human-centered skills such as creativity, collaboration, and adaptability, supported by digital technology. However, culinary vocational learning remains fragmented, with these components often developed separately. This study aims to develop an integrative conceptual model to address this gap. This research adopts a qualitative conceptual approach supported by a systematic literature-based synthesis. Data were collected from Scopus, Google Scholar, and SINTA using structured keywords. The selected studies were analyzed through content analysis and thematic synthesis to identify key components and relationships for model development. The study proposes the Integrated Culinary Deep Learning Model (ICDLM), which consists of four core dimensions: culinary technical skills, soft skills (creativity and collaboration), digital technology integration, and project-based learning. These components are structured as an interconnected system that enables holistic and meaningful learning. The findings indicate that integrative approaches are more effective than fragmented methods in developing higher-order competencies such as critical thinking, adaptability, and innovation. Theoretically, this study contributes an integrative framework for vocational education. Practically, the ICDLM model provides guidance for designing adaptive and industry-relevant culinary learning. Future research is recommended to empirically validate the model.
Contextual Media-Based Deep Learning in Elementary Education: A Qualitative Study of Meaningful and Joyful Learning through an Ecoenzyme Project Nela Rosita; Harun Joko Prayitno; Indri Indri; Khoirul Anwar; Fajar Riza Ul Haq; Suyanto Suyanto; Azmi Khusnani
Journal of Deep Learning Vol. 2, No. 2, December 2026
Publisher : Universitas Muhammadiyah Surakarta in Collaboration with Kemdikdasmen RI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/jdl.v2i2.15578

Abstract

The implementation of Deep Learning in elementary education requires learning experiences that are meaningful, engaging, and relevant to students' daily lives. However, research on contextual media-based Deep Learning in elementary schools remains limited. This study aimed to explore the implementation of Deep Learning through contextual media using an ecoenzyme project in elementary education. A qualitative descriptive approach was employed at Elementary School Muhammadiyah Program Khusus Pracimantoro. Data were collected through observations, semi-structured interviews, and documentation, and analyzed using the interactive model of Miles et al. with methodological triangulation to ensure data credibility. The findings reveal that contextual media promote meaningful learning by connecting instructional content with authentic experiences, thereby enhancing students' conceptual understanding, critical thinking, and reflective learning. In addition, the ecoenzyme project fostered joyful learning by improving students' motivation, collaboration, communication, and environmental awareness. The study also demonstrates that effective Deep Learning can be implemented through simple contextual media without relying on sophisticated digital technologies. These findings contribute to the development of student-centered learning strategies and provide practical implications for implementing Deep Learning in elementary education.
Co-Authors Azmi Khusnani Bayu Wardhana Harun Joko Prayitno Heri Susanto Herlina Tri Wulandari Indri Indri Indri Khoirul Anwar Nela Rosita Suyanto Suyanto