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JPTK: Jurnal Pendidikan Teknologi dan Kejuruan
Published by Universitas Negeri Yogyakarta
ISSN : 08544735     EISSN : 24772410     DOI : -
Core Subject : Education,
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Jurnal Pendidikan Teknologi dan Kejuruan (JPTK) is a journal of Technical and Vocational Education and Training (TVET) which is a double-blind peer-reviewed journal. The journal invites authors throughout the world to exchange and disseminate theoretical and practice-oriented topics relevant to: (1) teaching and learning in TVET, (2) evaluation, assessment and certification in TVET, (3) human resources management in TVET, (4) vocational education resources in TVET and (5) contemporary issues in TVET
Arjuna Subject : -
Articles 382 Documents
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Singular Value Decomposition in Machine Leaning for Image Compression in Vocational Tourism Batik Archiving Sudira , Putu; Sahria, Yoga; Fajaryati, Nuryake; Hakim, Septian Rahman; Nursusanto, Stevanus Widuri; Mhd Salim, Mohamad Hidir; Astuti, Rahayu Fuji
Jurnal Pendidikan Teknologi dan Kejuruan Vol. 31 No. 2 (2025): (October)
Publisher : Faculty of Engineering, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jptk.v31i2.95700

Abstract

The digital archiving of batik products in vocational tourism environments requires efficient image compression techniques that maintain critical visual information, including complex motifs, color patterns, and texture details. This study aims to investigate the application of Singular Value Decomposition (SVD) as a machine learning based approach for image compression in the digital archiving of batik products from the Sundhullangit Batik Vocational Tourism Village. An experimental research design was adopted using digital batik images obtained through direct image acquisition. The research stages comprised image pre-processing, image compression using a truncated Singular Value Decomposition model with varying rank values, and reconstruction of the compressed images. The performance of the compression model was evaluated using objective image quality metrics, namely Mean Squared Error, Peak Signal-to-Noise Ratio, and Structural Similarity Index, while compression efficiency was measured using the compression ratio. The results indicate that higher rank values enhance reconstructed image quality, reflected by lower reconstruction error and higher structural similarity, but reduce compression efficiency. Conversely, lower rank values achieve higher compression ratios at the cost of reduced visual fidelity. Overall, the findings demonstrate that Singular Value Decomposition offers an effective balance between image quality preservation and data size reduction. This study concludes that the proposed method is suitable for supporting sustainable and high-quality digital archiving of batik products within vocational tourism-based cultural heritage systems.
Impacts of Artificial Intelligence Integration on Teaching Practices and Student Engagement in Digitally Transformed Educational Settings Heri Nurdiyanto; Leonel Hernandes; Aktansi Kindiasari
Jurnal Pendidikan Teknologi dan Kejuruan Vol. 31 No. 1 (2025): (May)
Publisher : Faculty of Engineering, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jptk.v31i1.96073

Abstract

This study explores how the integration of artificial intelligence (AI) into digitally transformed educational settings reshapes everyday teaching practices and influences student engagement in real learning contexts. Rather than treating AI as a standalone technological upgrade, the study situates its use within the broader transformation of digital learning environments, where platforms, automated tools, and data-driven systems increasingly mediate classroom interactions. The findings show that AI integration gradually shifts the role of teachers from primarily delivering content toward designing learning experiences, guiding students’ learning processes, and responding to more diverse patterns of participation. In practice, AI-supported tools help streamline routine instructional tasks, open space for more personalized interaction, and provide timely support to students with different learning needs. At the same time, the results indicate that student engagement does not automatically improve simply because AI is introduced. Engagement grows when AI tools are meaningfully aligned with pedagogical goals, integrated into everyday learning activities, and supported by teachers’ readiness to adapt their instructional strategies. The study also highlights emerging tensions, including uneven levels of student participation, varying degrees of teacher confidence in using AI-based systems, and concerns about over-reliance on automated support. Overall, the findings suggest that the impact of AI in digitally transformed educational settings is shaped less by the technology itself than by how it is embedded in teaching practices and learning cultures, pointing to the importance of thoughtful integration in sustaining meaningful student engagement.

Page 39 of 39 | Total Record : 382

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