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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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Strategies and Development of the Deep Learning Approach in Vocational High Schools in the Era of Global Computing Joko Budi Utomo; Harun Joko Prayitno; Indri Indri
Journal of Deep Learning Vol. 1, No. 1, June 2025
Publisher : Universitas Muhammadiyah Surakarta in Collaboration with Kemdikdasmen RI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/jdl.v1i1.10855

Abstract

The transformation of vocational education in the era of the Industrial Revolution 4.0 demands the integration of learning approaches that go beyond cognitive aspects to include affective and contextual dimensions. This study aims to formulate relevant and applicable strategies for developing a deep learning approach tailored to Vocational High Schools (SMK) in Indonesia. Drawing on the principles of mindful, meaningful, and joyful learning, this research presents a conceptual and practical synthesis that can be implemented in vocational education. The findings indicate that project-based learning, digital reflection, and cross-disciplinary collaboration are effective strategies in fostering students' conceptual understanding and applied skills. Authentic assessments based on portfolios, presentations, and reflective journals are also deemed more representative in measuring deep learning outcomes. On the other hand, teacher capacity-building through transformative pedagogical training, socio-emotional reinforcement, and the formation of Professional Learning Communities is essential for successful implementation. The integration of digital technologies in vocational learning has proven to expand learning spaces and strengthen the connection between schools and industries. This study recommends strengthening policy and infrastructure support to ensure the sustainability of the deep learning approach in SMK as an adaptive, reflective, and relevant learning strategy in the digital era.
Deep Learning: Policies, Concepts, and Implementation in Senior High Schools in Indonesia Puthut Prihantoro; Harun Joko Prayitno; Indri Indri; Dian Artha Kusumaningtyas
Journal of Deep Learning Vol. 1, No. 1, June 2025
Publisher : Universitas Muhammadiyah Surakarta in Collaboration with Kemdikdasmen RI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/jdl.v1i1.10964

Abstract

The transformation of education in the era of the Industrial Revolution 4.0 and Society 5.0 demands a more holistic, adaptive, and relevant learning approach aligned with the needs of the 21st century. One emerging approach is deep learning, which, in the educational context, does not refer to artificial intelligence technology, but rather to a learning paradigm that emphasizes deep understanding, critical thinking, collaboration, and the interconnection of cognitive, emotional, and social aspects. This study aims to examine the concept, implementation, and challenges of deep learning in Indonesian education through an analysis of theoretical literature and contextual empirical findings. The study identifies three key pillars that underpin the deep learning approach: mindful learning, meaningful learning, and joyful learning, which work synergistically to create a humanistic and transformative learning experience. The findings show that the implementation of deep learning contributes positively to improving students’ learning motivation, engagement, and reflective thinking skills. Although national policies such as the Merdeka Curriculum have incorporated these principles, practical implementation still faces challenges, particularly concerning teacher competencies and resource availability. Therefore, strengthening educators' capacity and providing adequate learning infrastructure are crucial for the sustainable implementation of deep learning in Indonesia.
Opportunities and Challenges for the Development of Deep Learning in Vocational Schools: Drivers of Learning Innovation in the Industrial Era 4.0 Dwi Andriyani Andriyani; Harun Joko Prayitno; Minsih Minsih; Arif Jamali; Vismaia S. Damayanti; Taqiya Dipsatara; Fajar Gemilang Pradana
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.v1i2.11027

Abstract

Advances in artificial intelligence technology, especially deep learning, have brought about significant transformations in various sectors of life, including in the world of education. Vocational High Schools (SMK), as educational institutions that focus on developing vocational skills, have ample opportunities to optimize deep learning in the learning process and development of students' abilities. This paper aims to analyze the opportunities and development of deep learning in vocational schools, emphasizing the advantages, obstacles, and strategic steps that can be taken in its implementation. The analysis shows that the integration of deep learning into the vocational school curriculum can enrich the learning experience through personalized learning, evaluation automation, student achievement analysis, and the development of adaptive and data-based learning systems. In vocational sectors such as engineering, automotive, information technology, and multimedia, the use of deep learning can assist in simulation, data analysis, and product development based on artificial intelligence. However, the main challenges include the limited number of experts in the field of deep learning, the need for adequate technological infrastructure, and the availability of quality data. Therefore, collaboration between schools, industry, and government is essential to provide training, laboratory facilities, and curriculum development that is in accordance with the needs of industry 4.0. With the right approach, the implementation of deep learning in vocational schools not only strengthens the competitiveness of alumni in the job market, but also facilitates the emergence of new innovations and creativity among students. Finally, the potential of deep learning in vocational schools is an ongoing revolution, which can change the paradigm of vocational education to a digital era that is more adaptive and responsive to the dynamics of the industry in the future. Keywords: Deep Learning, Vocational High School, Vocational Education, Innovation, Industry 4.0
Deep Learning: Implementation and Impact in Islamic Junior High Schools Erda Farid Hidayani; Harun Joko Prayitno; Trisakti Handayani
Journal of Deep Learning Vol. 1, No. 1, June 2025
Publisher : Universitas Muhammadiyah Surakarta in Collaboration with Kemdikdasmen RI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/jdl.v1i1.11077

Abstract

This study aims to describe the implementation of deep learning strategies at the secondary level within an Islamic educational setting, specifically at SMP Islam Ar‑Rahmah. A descriptive qualitative approach was employed to examine in depth the teaching–learning processes integrated with Islamic values and the school’s sociocultural context. Data were gathered through participatory observation, in‑depth interviews, and the documentation of instructional materials. The findings reveal that deep learning was effectively realized through Problem‑Based Learning (PBL) and Project‑Based Learning (PjBL) in both the natural science (IPA) and social science (IPS) curricula. Students actively engaged in contextualized projects—such as the Socio Project Board initiative and water‑filtration experiments—which fostered critical thinking, collaborative teamwork, and data‑driven decision‑making competencies. Moreover, the integration of religious programming and boarding‑school activities contributed to the development of intrapersonal and interpersonal skills that align with the core values of Pancasila students. Nevertheless, challenges persist, particularly with respect to instructional design and the availability of teacher training. Consequently, systemic support from educational institutions and policymakers is necessary to bolster the sustainable and meaningful implementation of deep learning in Islamic schools.
Toward a Theoretical Model of Deep Mathematical Thinking: Integrating Deep Learning and Mathematical Reasoning Frameworks Yoga Tegar Santosa; Muhammad Noor Kholid; Naufal Ishartono; Munaaya Fitriyya; Iwan Junaedi
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.v1i2.11142

Abstract

In the context of 21st-century education, deep mathematical thinking is key to developing higher-order cognitive abilities. However, current mathematics learning predominantly focuses on procedural skills rather than conceptual understanding and reflection, often hindering the optimal development of students' deep mathematical thinking. Therefore, this study aims to develop a theoretical Deep Mathematical Thinking model through the integration of deep learning pedagogical principles and the mathematical thinking framework. Employing a systematic theoretical synthesis approach, the developed model identifies and interconnects core elements of deep learning, such as conceptual connectivity, intrinsic motivation, and metacognition with components of mathematical thinking, including reasoning, generalization, representation, and abstraction. The outcome is a three levels hierarchical Deep Mathematical Thinking model: the foundational level, the applied cognitive level, and the integrative level. This model offers theoretical and practical implications for curriculum design, assessment, and instructional practices. While still conceptual and requiring further empirical validation, the model is flexible and adaptable across diverse educational levels and contexts, positioning it as a potentially robust conceptual framework for developing reflective and meaningful mathematics learning.
Developing Deep Learning-Based Worksheets to Improve Higher-Order Thinking Skills in Elementary Social Studies Jatmiko Ananda Chosya; Takiddin Takiddin
Journal of Deep Learning Vol. 1, No. 1, June 2025
Publisher : Universitas Muhammadiyah Surakarta in Collaboration with Kemdikdasmen RI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/jdl.v1i1.11156

Abstract

This study aims to develop and evaluate a student worksheet (LKPD) based on deep learning principles to enhance Higher-Order Thinking Skills (HOTS) among fourth-grade students in a madrasah Social Studies context. Employing a Research and Development (R&D) approach with the ADDIE model, the study proceeded through five systematic stages: Analysis, Design, Development, Implementation, and Evaluation. The LKPD was validated by experts in content, language, and media, resulting in an overall validity score of 84.6%, indicating high feasibility for classroom use. In the implementation phase, 30 students participated in pretest and posttest activities. Statistical analysis showed a significant improvement in student learning outcomes, with a t-test significance value of 0.000 and an average N-Gain score of 0.32, classified as moderate. Pedagogical features such as differentiated learning strategies, comic-based apperception, and audio-visual integration through QR codes were embedded in the LKPD to target HOTS domains of analysis, evaluation, and creation. Compared to previous studies, this research extends the application of HOTS-based learning materials into the context of Islamic primary education and demonstrates the pedagogical potential of deep learning in fostering critical and creative thinking. These findings support the broader adoption of multimodal, student-centered instructional designs to meet the demands of 21st-century education.
Implementation of Deep Learning in Education: Towards Mindful, Meaningful, and Joyful Learning Experiences Lukie Masayu Andayanie; Muhammad Syahriandi Adhantoro; Eko Purnomo; Ganno Tribuana Kurniaji; Adi Jufriansah
Journal of Deep Learning Vol. 1, No. 1, June 2025
Publisher : Universitas Muhammadiyah Surakarta in Collaboration with Kemdikdasmen RI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/jdl.v1i1.11157

Abstract

The deep learning approach in education has emerged as a promising pedagogical framework for transforming the learning experience to be more holistic, reflective, and aligned with the needs of the twenty‑first century. This study seeks to conceptually explore how the principles of mindful, meaningful, and joyful learning may be integrated through deep learning–based instructional strategies. The methodology comprises a systematic literature review and a conceptual analysis of pertinent academic publications, research reports, and innovative educational practices. Findings reveal that strategies such as personalized learning, project‑based learning, game‑based learning, and community engagement effectively enhance student engagement, critical thinking skills, and intrinsic motivation. Nonetheless, the implementation of deep learning approaches continues to encounter significant challenges, including educator preparedness, limitations in technological infrastructure, and the misalignment of conventional assessment systems. This article recommends the enactment of progressive policy support, the enhancement of professional capacity among educators, and further empirical research to evaluate the efficacy of deep learning strategies across broader contexts. It is anticipated that these findings will contribute to the advancement of more adaptive, inclusive, and empowering educational policies and practices.
Learning Innovation in Global Citizenship Education by Integrating Deep Learning Approach Sutrisno Sutrisno; Sunarto Sunarto; Dyah Mustikasari
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.v1i2.11224

Abstract

Conventional learning methods in dealing with the complexity of dynamic and multidimensional global issues in learning global citizenship education are still ineffective, this can be seen from the low understanding and awareness of students in solving global issues that occur. This research aims to develop and test learning innovations by integrating deep learning approaches to improve students' understanding and participation in the context of global citizenship. The research method used was quantitative research with an experimental approach. The research subjects consisted of senior high school students in a sample school. The research instruments included questionnaires, comprehension tests, and observations used to measure the effectiveness of learning before and after the application of the deep learning approach. The collected data were analyzed using descriptive and inferential statistics to assess the improvement of learning outcomes. The results showed that the integration of deep learning approach in global citizenship education significantly improved students' concept understanding, critical thinking skills, and learning motivation. Students become more active and able to connect civic education materials with contemporary global issues more deeply. The implication of this research finding is that the application of deep learning technology can be an effective innovative strategy in developing global citizenship education that is adaptive and responsive to the challenges of the times. This encourages educators to utilize artificial intelligence technology in the learning process to create a more interactive and meaningful learning experience.
Deep Learning vs Differentiated Learning: Learning Innovation in Islamic Boarding School-Based Middle Schools Murwanto Setyo Nugroho; Harun Joko Prayitno; Koesoemo Ratih; Muhammad Samsudin
Journal of Deep Learning Vol. 1, No. 1, June 2025
Publisher : Universitas Muhammadiyah Surakarta in Collaboration with Kemdikdasmen RI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/jdl.v1i1.11246

Abstract

This study aims to examine the implementation of deep learning and differentiated learning approaches in Islamic boarding school-based junior high schools (SMP) in order to improve the quality of meaningful, contextual, and humanistic learning. Using a descriptive qualitative approach, data were collected through in-depth interviews, classroom observations, and documentation studies in three Islamic boarding school junior high schools that integrate national and religious curricula. The results of the study indicate that learning strategies such as project-based learning, reflective discussions, and collaborative activities are effective in building conceptual understanding and spiritual values ​​of students. Differentiated learning enables the creation of an inclusive learning environment by adjusting content, processes, and products according to students' learning profiles. Implementation challenges arise in terms of time constraints, dual curriculum burdens, and teacher professional readiness. Authentic project-based and portfolio-based evaluations are more relevant in measuring cognitive, affective, and spiritual competencies holistically. This study recommends the need for educational policies that are more adaptive to the characteristics of Islamic boarding schools, including contextual teacher training, curriculum flexibility, and an inclusive and sustainable evaluation system. These findings confirm that deep learning and differentiated learning have great potential in transforming Islamic boarding school education towards 21st-century learning that is more humanistic, reflective, and transformative.
Visual and Contextual Learning for Deep Learning Education: A Unified Tool for Theory–Practice Integration Itang Enrico Pradana Mahardhika; Harun Joko Prayitno; Indri Indri; Muhammad Ramadhan Fitriyan
Journal of Deep Learning Vol. 1, No. 1, June 2025
Publisher : Universitas Muhammadiyah Surakarta in Collaboration with Kemdikdasmen RI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/jdl.v1i1.11556

Abstract

The rapid advancements in artificial intelligence, particularly in deep learning, demand learning methods that are not only theoretical but also applicative and intuitive. This study aims to design and develop an interactive deep learning instructional tool that integrates real-world case studies and dynamic visualizations to enhance users’ conceptual understanding and practical skills. The research employs a development methodology with a mixed-methods approach, adopting the ADDIE model and iterative formative evaluation based on Tessmer’s framework. The developed tool incorporates three primary case studies: image classification, sentiment analysis, and time-series prediction. Each case study features an interactive interface that allows users to explore data, adjust model parameters, and visualize training processes and results in real time. Formative evaluation results demonstrate the tool’s effectiveness in improving learning engagement, understanding of deep learning model mechanisms, and motivation to explore the topic further. These findings underscore the significance of experiential and visualization-based approaches in cutting-edge technology education while contributing to the development of adaptive AI-based learning media.

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