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All Journal JPTK: Jurnal Pendidikan Teknologi dan Kejuruan International Journal of Engineering, Science and Information Technology Journal of Research in Social Science and Humanities Inisiatif: Jurnal Dedikasi Pengabdian Masyarakat Advance Sustainable Science, Engineering and Technology (ASSET)
Yoga Sahria
Universitas Negeri Yogyakarta
Religion Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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AI-Supported Practical Learning in Vocational Education: Challenges and Design Principles Yunda Michel Rismawati; Nunung Setiawati; Erik Yumita Sudharta; Putu Sudira Fajaryati; Pipit Utami; Yoga Sahria
Journal of Research in Social Science and Humanities Vol 5, No 3 (2025)
Publisher : Utan Kayu Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47679/jrssh.v5i4.565

Abstract

Artificial intelligence (AI) is increasingly integrated into vocational education to support practical skill development and technology-enhanced training environments. However, existing studies remain fragmented across different technological applications and provide limited conceptual understanding of how AI technologies collectively support practical learning processes. This study conducts a systematic literature review following the PRISMA 2020 guideline to synthesize current evidence on AI-supported practical learning in vocational education. Seventeen studies published between 2018 and 2025 were identified from the Scopus database and analyzed through thematic synthesis. The findings indicate that AI technologies are commonly implemented through simulation platforms, intelligent tutoring systems, learning analytics and performance monitoring tools, adaptive learning systems, and AI-supported experiential learning environments. Five recurring pedagogical mechanisms were identified: simulation-based practice, intelligent skill guidance, performance feedback and analytics, adaptive learning pathways, and experiential or work-based learning. The review also highlights implementation challenges related to infrastructure, data availability, ethical concerns, and teacher AI literacy. Based on these findings, a conceptual framework is proposed to explain how AI technologies support practical learning and competency development in vocational education. The synthesis also suggests opportunities for integrating emerging approaches such as multimodal learning analytics and facial expression recognition (FER) to better understand learner engagement during practical training activities.
Camera-Based Smart Mirror with Machine Learning for Postural Analysis: System Development and Reliability Evaluation Fitri Yani; Yoga Sahria; Siti Nadhir Ollin Norlinta; Riska Risty Wardhani
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.3097

Abstract

Early postural assessment using camera-based systems remains technically challenging due to variability in user positioning and limited evaluation of measurement repeatability. This study presents the development and repeatability evaluation of a smart mirror system for automated postural analysis using pretrained pose estimation and rule-based geometric classification. The system consists of a fixed camera mounted above a mirror and a connected computing device for real-time processing and visual feedback. Anatomical landmarks were detected from standardized anterior, posterior, and lateral views using an AI-based pose estimation model, and postural asymmetry was quantified using bilateral distance ratios and angular deviation thresholds derived from literature. Reliability was evaluated through repeated measurements to assess the consistency of landmark detection and postural classification outputs. Forty adolescents (age 12.8 ± 0.56 years; 28 males, 12 females) participated in present study. The system intra-rater reliability was evaluated by calculating Intraclass Correlation Coefficients (ICC) for the landmark data and Cohen's Kappa for posture classifications. The system demonstrated excellent reliability for key landmarks in scapula (ICC = 0.98, 95%CI 0.97-0.99) and hip-knee-ankle (ICC = 0.98, 95%CI 0.98-0.99). The classifications for scoliosis assessment also showed excellent agreement (κ = 0.90). These results indicate that the proposed system can produce repeatable posture measurements under controlled conditions; however, this study evaluates repeatability only and does not assess diagnostic accuracy or clinical validity. Further validation against clinical reference standards is required before broader application. 
Application of Singular Value Decomposition for Image Compression of Yogyakarta Cosmological Axis in Digital Learning in Vocational Education Yoga Sahria; Putu Sudira; Mohamad Hidir Mhd Salim
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1732

Abstract

This study examines the application of the Singular Value Decomposition (SVD) method as a digital image compression technique on the Yogyakarta Cosmological Axis object which is used as a digital learning medium in vocational education. The background of this study is based on the need for high-quality visual media with efficient file sizes for easy storage, transmission, and access through digital-based learning systems. The study uses an experimental quantitative approach with data in the form of high-resolution digital images processed through SVD-based compression stages. The research procedure includes image transformation into matrix form, matrix decomposition using SVD, selection of a number of dominant singular values (ranks), and reconstruction of the compressed image. The research data were analyzed using image quality evaluation parameters, namely Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Compression Ratio (CR). The results show that an increase in the rank value is directly proportional to an increase in the quality of the reconstructed image, as indicated by a decrease in the MSE value and an increase in the PSNR and SSIM values. Conversely, a decrease in the rank value results in a higher compression rate but is followed by a degradation in the visual quality of the image. Experimental data also shows that most of the visual information of an image can be represented by a small number of principal singular values, thus allowing for significant file size reduction without losing the important visual structure of the image object. Visually, the compressed image at a medium rank value is still considered suitable for use as a learning medium because the main details, object contours, and visual characteristics of the Yogyakarta Cosmological Axis can still be recognized well. These findings prove that the SVD method is effective as a mathematical-based image compression technique to support the development of efficient, informative, and contextual digital learning media based on local wisdom in vocational education
Pelatihan Artificial Intelligence Untuk Meningkatkan Kapasitas Inovasi Pembelajaran Vokasional Kepala Sekolah SMK Di Provinsi Bali Yoga Sahria; Putu Sudira; Ayu Niza Machfauzia
Inisiatif : Jurnal Dedikasi Pengabdian Masyarakat Vol 5 No 1 (2026): Inisiatif : Jurnal Dedikasi Pengabdian Masyarakat
Publisher : Pusmedia Group Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61227/inisiatif.v5i1.812

Abstract

Digital transformation in vocational education requires vocational school principals to be competent in utilizing Artificial Intelligence (AI) to improve the quality of learning and school management. However, understanding and skills in utilizing AI among principals are still diverse, so a capacity building program that is relevant to field needs is needed. This Community Service (PkM) activity aims to improve the literacy, skills, and motivation of vocational school principals in Bali Province in developing AI-based vocational learning innovations. The activity was carried out at the Bali Provincial Education, Youth, and Sports Office and involved 30 top vocational school principals representing various regencies/cities in Bali. The Participatory Action Research (PAR) method used included training, demonstrations, hands-on practice, group discussions, and mentoring in developing follow-up plans for AI implementation in schools. Training materials included the use of generative AI, NotebookLM, digital learning media development, interactive presentations, and learning simulation games. Evaluation results showed an average participant satisfaction score of 4.80 on a scale of 5.00 with a 100% satisfaction rate. Participants demonstrated increased insight, skills, and motivation in applying AI and developing various learning innovation plans that will be implemented and disseminated in their respective schools.
Optimization of Image Compression Using K-Means Clustering for Digital Heritage Archives Yoga Sahria; Putu Sudira; Priyanto
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2772

Abstract

Preserving digital cultural assets requires efficient compression to minimize storage and bandwidth costs. However, existing studies rarely evaluate K-Means Clustering on structurally complex objects such as the Prambanan Temple, leaving a research gap in assessing its performance against standard codecs. This study introduces a novel optimized K-Means pipeline with adaptive cluster selection and improved centroid initialization for compressing high-detail temple imagery. The method groups pixels based on color proximity, reducing redundancy while preserving key structural patterns. Experiments show that K-Means achieves PSNR 28.08–30.65 dB and SSIM 0.86–0.92, outperforming baseline JPEG at similar file sizes PSNR 26–28 dB, SSIM 0.80–0.87. This quantitative comparison demonstrates the model’s superior perceptual retention in textured stone regions. The methodological contribution lies in combining spatial–chromatic feature weighting with iterative centroid refinement, which increases cluster stability and reduces quantization artifacts. Findings confirm K-Means as a viable alternative for controlled-distortion compression. In conclusion, the proposed approach provides practical engineering implications, enabling reduced storage footprints, predictable reconstruction quality, and integration into hybrid compression pipelines for large-scale digital imaging systems.
Singular Value Decomposition in Machine Leaning for Image Compression in Vocational Tourism Batik Archiving Putu Sudira; Yoga Sahria; Nuryake Fajaryati; Septian Rahman Hakim; Stevanus Widuri Nursusanto; Mohamad Hidir Mhd Salim; Rahayu Fuji Astuti
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.
Co-Authors Ayu Niza Machfauzia Erik Yumita Sudharta Fitri Yani Mohamad Hidir Mhd Salim Mohamad Hidir Mhd Salim Nunung Setiawati Nuryake Fajaryati Pipit Utami Priyanto Putu Sudira Putu Sudira Fajaryati Rahayu Fuji Astuti Septian Rahman Hakim Siti Nadhir Ollin Norlinta Stevanus Widuri Nursusanto wardhani, Riska risty Yunda Michel Rismawati