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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 9 Documents
 1 
Search results for , issue "vol. 1, no. 2, december 2025" : 9 Documents clear
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
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.
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-Based Learning Strategies in Realizing Meaningful, Critical, and Enjoyable Learning Rizwatul Hasanah; Harun Joko Prayitno; Suni Tuti; Sarilan Sarilan; Velandani Prakoso
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.v1i2.11572

Abstract

This study aims to examine the form and scope of the application of Deep Learning in the Vocational High School (SMK), especially at SMK Mantingan, through a literature study approach. Deep Learning, as part of artificial intelligence, has great potential in the world of vocational education, especially in supporting technology-based learning and improving student competencies in the digital field. This study relies on various literature sources such as scientific journals, books, articles, and relevant documents to examine the concepts, applications, and challenges faced in the integration of Deep Learning in vocational schools. The results of the study show that although the application of Deep Learning in vocational schools, including at SMK Mantingan, is still relatively new and limited, there are great opportunities for development in project-based learning and improving students' digital literacy. The scope of implementation includes the introduction of basic concepts, the use of supporting software, and the use of simple models for educational purposes. This study recommends the need for teacher training, the provision of adequate infrastructure, and collaboration with higher education institutions or industry to expand the application of this technology in vocational schools.
Fundamental Concepts of Deep Learning Principles in Advancing Holistic Education Practices Siti Fatika Alim; Harun Joko Prayitno; Abdul Mu’ti; Anam Sutopo; Weni Hastuti
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.v1i2.11597

Abstract

This research aims to explore the application of the fundamendal concept of deep Learning (DL) in the context of holistic education in Indonesia. The method used is library research by collecting and analyzing information from various relevant written sources. The result show that the implementation of DL, These are three main principles in deep learning, namely mindful, meaningful, and joyful, which are interrelated and contribute to student character building. The mindful principle emphasizes awarness and reflection in the learning process, while the meaningful principle relates the subject matter to the context of student’s lives, and the joyful principles creates a fun learning atmosphere. These three principles not only ain to improve academic understanding, but also support student’s social and emotional development. The research also identifies challenges in implementating DL, including the need for minset change among educators and support of a conducive learning ecosystem. The findings are expected to provide new insights for educators and policy makers in creating a more inclusive and adaptive learning environment.
Reimagining 21st-Century Higher Education: A Literature-Based Exploration of Deep Learning for Cognitive, Social, and Emotional Skill Development Muhammad Affan Baihaqi; Harun Joko Prayitno; Naufal Ishartono; Sri Sat Titi Hamranani; Candra Anugrah Putra; Muhammad Nizaar
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.v1i2.11600

Abstract

The transformation of higher education in the era of global communication demands a paradigm shift in learning that not only focuses on content mastery but also emphasizes the development of higher-order thinking skills and readiness to face 21st-century challenges. This article aims to comprehensively examine the concept, urgency, and implementation strategies of deep learning in higher education. This study employs a qualitative approach through literature review by analyzing various relevant national scientific sources. The findings indicate that deep learning enhances students’ conceptual understanding, cognitive engagement, and the development of social and emotional dimensions in learning. Its implementation strategies include formulating learning objectives based on constructive alignment, utilizing digital technology, applying active learning methods such as Project-Based Learning and flipped classrooms, as well as incorporating authentic assessments. This article recommends the integration of deep learning as a holistic, adaptive, and contextual learning approach to improve the quality of higher education amid global complexities and technological advancements.
Strategies of Deep Learning to Foster Meaningful and Sustainable Education in the 21st Century Wafa Taqiyya; Ratnasari Dyah Utami; Muh Samsuri; Heri Siswanto
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.v1i2.11609

Abstract

The 21st century demands a transformation of learning paradigms that not only focus on academic outcomes but also emphasize the development of critical thinking, collaboration, creativity, and communication skills. This study aims to identify and formulate applicable and contextual deep learning strategies for 21st-century education, as well as to examine their effectiveness in promoting meaningful and sustainable learning. Employing a qualitative approach through literature review, data were collected from various relevant sources. The findings reveal that deep learning strategies—anchored in the principles of Meaningful Learning, Mindful Learning, and Joyful Learning—enhance the quality of interaction among students, learning materials, and teachers. The developed model consists of four stages: contextual exploration, conceptual elaboration, reflection, and action. These strategies are applicable across educational levels and emphasize students' active engagement in the learning process. The results show that the implementation of deep learning contributes to the development of students’ critical thinking, creativity, and adaptability. Furthermore, these strategies strengthen students’ collaborative and reflective capacities, creating a learning environment that is both enjoyable and relevant. The study concludes that deep learning strategies are not only aligned with the demands of modern education but also have the potential to build an inclusive and sustainable learning system. Their implementation supports improved social, economic, and environmental development while preparing young generations to face global challenges. Thus, deep learning serves as a bridge toward educational transformation centered on 21st-century competencies.
Enhancing GTAW Proficiency through ADDIE-Integrated Joyful Lear-ning Simulation: Empirical Insights from Vocational High School Implementation Joko Budi Utomo; Harun Joko Prayitno; Indri Indri; Adi Nur Cahyono; Nunuk Suryani; Ita Nuryana; Muhammad Rohmadi
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.v1i2.11661

Abstract

Welding practice learning in vocational schools often faces challenges such as limited equipment, time constraints, and safety risks. This study aims to implement a Welding Simulator using a joyful learning approach to improve students' practical competencies in GTAW (Gas Tungsten Arc Welding) at vocational high school level. The research is part of an ADDIE-based development process, with a focus on the implementation phase. The participants were 34 purposively selected 11th-grade students. Data were collected through practical tests (pre-test and post-test) and student perception questionnaires, analyzed using paired t-tests and descriptive statistics. The results showed a significant improvement in post-test scores compared to pre-test (from 64.2 to 77.35; p < 0.05). Students' perceptions of the media were highly positive, with an average rating of 4.00 out of 5. The simulator effectively enhanced students’ welding skills while creating an engaging and enjoyable learning environment. The study concludes that the joyful learning-based welding simulator is effective as a practical learning tool in vocational education.
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.

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