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Harun Joko Prayitno
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Journal of Deep Learning
Published by Universitas Muhammadiyah Surakarta
ISSN : -     EISSN : 31107591     DOI : 10.23917/jdl
Core Subject : Education, Social,
Education Languange, Linguistic, Communication & Media Social Sciences
The Journal of Deep Learning is dedicated to advancing research on deep learning as a methodological approach in education and AI-based technological development. We emphasize deep learning integrated with artificial intelligent (AI) that fosters meaningful, joyful, and mindful education—rooted in authentic, holistic learning experiences that support students’ spiritual, cognitive, social, and emotional development. 1.⁠ ⁠Fostering Mindful Education Practices We seek contributions that explore how deep learning methodologies can cultivate mindfulness in educational settings. This encompasses promoting self-awareness, reflective practices, metacognition, and emotional regulation to support holistic student development. 2.⁠ ⁠Deep Learning for Meaningful Education We invite articles that examine how deep learning methodologies such as inquiry-based learning, problem-based learning, and project-based learning support students in constructing meaningful knowledge. Studies may focus on developing students’ critical thinking, conceptual understanding, and ability to connect new knowledge with real-life contexts. 3.⁠ ⁠Promoting Joyful Learning Experiences We welcome research on how deep learning approaches can foster joy in learning by encouraging curiosity, creativity, and intrinsic motivation. This includes studies on creating engaging learning environments, designing stimulating learning activities, and supporting positive teacher-student interactions. 4.⁠ ⁠Integration of Technology in Deep Learning Pedagogy We encourage studies that examine how technology such as learning management systems, digital tools, and educational apps can be used to support deep learning methodologies. Submissions should highlight how technology enhances or challenges the implementation of deep, meaningful, joyful, and mindful learning. 5.⁠ ⁠Evaluation and Assessment of Deep Learning Outcomes We welcome research on effective evaluation and assessment strategies aligned with deep learning approaches. This includes authentic assessment, formative assessment, and performance-based tasks that capture students’ understanding, creativity, and reflective abilities.
Arjuna Subject : Ilmu Sosial - Pendidikan
Articles 28 Documents
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Designing Mathematics Teaching through Deep Learning Pedagogy: Toward Meaningful, Mindful, and Joyful Learning Torang Siregar; Ahmad Fauzan; Yerizon Yerizon; Syafriandi Syafriandi
Journal of Deep Learning Vol. 1, No. 2, December 2025
Publisher : Universitas Muhammadiyah Surakarta in Collaboration with Kemdikdasmen RI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/jdl.v1i2.11969

Abstract

Mathematics education in the 21st century requires an approach that focuses not only on procedural mastery but also on deep conceptual understanding, critical thinking skills, and the ability to transfer knowledge across various life contexts. This article discusses the design of mathematics learning based on a deep learning approach not in the context of artificial intelligence, but as a pedagogical approach integrating three core principles: meaningful, mindful, and joyful. Meaningful learning is built through contextual activities and open-ended problems that connect mathematical concepts to real-world situations. Reflective and process-aware learning is facilitated through discussion, metacognitive reflection, and evaluation. Meanwhile, a joyful learning environment is created through active participation, open exploration, and recognition of diverse student thinking styles. The theoretical framework includes constructivism, transfer theory, metacognition, and process-based assessment. The implementation of this approach requires a transformation of the teacher's role into a facilitator, a flexible and meaning-oriented curriculum, and a supportive, collaborative learning environment. It is concluded that the deep learning approach, through the principles of meaningful, mindful, and joyful learning, can enhance student engagement, deepen understanding, and foster a positive attitude toward mathematics.
Implementation of Deep Learning Strategies for Developing Empathy and Collaboration Students Retno Megawati; Harun Joko Prayitano; Fajar Riza Ul Haq; Oman Suryaman
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.v2i1.12031

Abstract

The aims of this study is to explore how the implementation of deep learning strategies to foster empathy and collaboration among second-grade elementary school students. Anchored in Vygotsky's Sociocultural Theory and the Social Emotional Learning (SEL) framework, the research focuses on how project-based and inquiry-based learning approaches contribute to the development of students’ social-emotional skills. The primary objective is to examine how these pedagogical strategies create meaningful learning experiences that nurture students’ ability to understand others’ perspectives and work cooperatively with peers. The study also explores the teacher’s role in facilitating deep learning through guided interaction within the Zone of Proximal Development (ZPD), where support is tailored to students’ developmental readiness. Data were collected through classroom observations, semi-structured interviews, and analysis of student-created learning artifacts. Thematic analysis revealed that deep learning strategies provide authentic contexts for social interaction, enabling students to engage in perspective-taking and constructive collaboration. As a result, students exhibited notable growth in their ability to empathize and collaborate, indicating that well-structured, emotionally responsive instruction can enhance both cognitive and interpersonal development in early primary education.
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.
Teacher Readiness for Deep Learning Implementation in Indonesian Education: A Systematic Narrative Review of Dimensions, Barriers, and Enabling Factors Waloyo Waloyo; Harun Joko Prayitno; Toni Toharudin; Yuli Rahmawati
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.v2i1.13100

Abstract

The transformation of twenty-first century education requires a shift from rote-based instruction toward deep learning that fosters critical thinking, creativity, and meaningful understanding. In Indonesia, this shift aligns with national reforms such as Merdeka Belajar. However, its success depends on teachers’ readiness to implement deep learning practices. This study aims to analyze the dimensions, barriers, and enabling factors of teacher readiness in this context. This study employs a systematic narrative review with thematic analysis of literature published between 2019 and 2025, sourced from Google Scholar, Scopus, DOAJ, and ERIC. From 127 identified articles, 35 studies met the inclusion criteria and were analyzed. The analysis is guided by three dimensions of readiness: cognitive (pedagogical knowledge), practical skills, and institutional support. The findings indicate that Indonesian teachers generally exhibit strong cognitive readiness but face challenges in practical implementation, particularly in designing authentic assessments and reflective learning activities. Institutional support remains uneven, especially in terms of infrastructure and professional development. Key barriers include limited training, infrastructure disparities, and traditional pedagogical practices, while enabling factors include supportive policies, teacher motivation, and professional learning communities. Comparative analysis shows that Indonesia performs relatively well in pedagogical knowledge but lags in practical readiness and institutional support compared to international contexts. In conclusion, enhancing teacher readiness requires an integrated approach that strengthens practical competencies and systemic support to achieve sustainable deep learning implementation.
Deep Learning Approach: Potentials and Challenges in Indonesian Education Izza Hanifa Astono; Sri Endah Wahyuningsih; Sri Handayani
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.v2i1.14135

Abstract

The transformation of education in the era of globalization and the Industrial Revolution 4.0 requires a paradigm shift from memorization-based learning toward approaches that emphasize deep understanding, active engagement, and the development of twenty-first century skills. The Deep Learning approach has emerged as a relevant pedagogical strategy to address these challenges in both academic and vocational education contexts. This study aims to comprehensively review the conceptual foundations, theoretical frameworks, empirical effectiveness, and the role of technology in implementing Deep Learning within Indonesia’s educational ecosystem. This research employs a literature review method by examining a range of scholarly articles and relevant policy documents, with a focus on deep learning practices, higher-order thinking development, and the integration of educational technologies. The findings indicate that the Deep Learning approach is supported by robust theoretical frameworks, such as the Deep Mathematical Thinking (DMT) model, which emphasizes conceptual connectivity, intrinsic motivation, and metacognition. Empirical evidence further demonstrates that the implementation of Deep Learning significantly enhances learning outcomes, cognitive engagement, and students’ conceptual understanding. Within vocational education, the integration of simulation technologies under the frameworks of Deep Learning and Joyful Learning has been shown to substantially improve students’ practical competencies and the overall quality of learning experiences. This study concludes that the integration of strong theoretical foundations, Deep Learning pedagogy, and adaptive learning technologies constitutes a key driver of effective and sustainable educational reform, aligned with global demands and future-oriented educational transformation.
Transformational Leadership, School Autonomy, and Deep Learning in Vocational Education: A Qualitative Case Study Nurahmad Fauzi; Bambang Sumardjoko; Sri Lahir; Suni Tuti; Adi Jufriansah; Bayu Wardhana; Iwan Junaedi; Ita Nuryana
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.v2i2.15528

Abstract

School autonomy is essential for enabling Vocational High Schools (SMKs) to respond to changing educational and labor-market demands. However, formal autonomy does not always translate into institutional flexibility and pedagogical innovation. This study analyzes principals’ transformational leadership strategies for strengthening school autonomy and creating conditions that support Deep Learning through meaningful, mindful, and joyful learning. A qualitative intrinsic case study design was employed, using semi-structured interviews, non-participant observations, and document analysis involving principals, vice principals, teachers, vocational program coordinators, and other relevant personnel. Data were analyzed using the interactive model of Miles, Huberman, and Saldaña. The findings reveal three interconnected strategies: (1) articulating an autonomy-oriented vision and reconstructing bureaucratic culture; (2) empowering teachers through participation, delegation, trust, and professional autonomy; and (3) expanding school autonomy through policy negotiation and external networking, particularly with education authorities and DUDI. These strategies created organizational conditions conducive to Deep Learning by supporting contextual and authentic learning, reflective engagement, and engaging and collaborative learning experiences. The study contributes to educational leadership scholarship by demonstrating how transformational leadership can translate formal school autonomy into organizational capacity that supports pedagogical transformation. Practically, the findings highlight distributed leadership, teacher empowerment, accountability, pedagogical flexibility, and strategic industry partnerships as important mechanisms for strengthening autonomous and responsive vocational education.
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.
Deep Learning as an Integrated Pedagogical Framework: Synthesizing Mindful, Meaningful, and Joyful Learning for Transformative Educational Experiences Eni Kurnianingsih; Harun Joko Prayitno; Abdul Mu’ti; Zakiyuddin Baidhawy; Adi Jufriansah
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.v2i2.16008

Abstract

The transformation of 21st-century education requires a shift from knowledge transmission toward learning approaches that emphasize deeper understanding, active engagement, and meaningful experiences. The deep learning approach has emerged as a pedagogical framework that integrates meaningful learning, mindful learning, and joyful learning to develop students’ cognitive, affective, and metacognitive dimensions. However, previous studies have often examined these dimensions separately, resulting in limited understanding of their integration within a comprehensive framework. This study aims to analyze the deep learning approach, identify its implementation patterns, challenges, and implications for improving educational quality. A descriptive qualitative approach was employed through literature synthesis, supported by teacher perceptions, classroom observations, and interviews. Data were analyzed using qualitative descriptive analysis and validated through technique triangulation. The findings reveal that deep learning effectively combines meaningful learning as a foundation for conceptual understanding, joyful learning as a driver of engagement, and mindful learning as a mechanism for reflection and metacognitive development. However, implementation remains dominated by meaningful and joyful learning, while mindful learning receives limited attention. This study highlights that the main challenge of deep learning lies in translating its principles into systematic reflective practices. The findings contribute to pedagogical development by emphasizing the importance of balanced integration among the three dimensions to create meaningful, enjoyable, and sustainable learning experiences.

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