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All Journal Jurnal Teknik Industri Jurnal Pendidikan Vokasi Jurnal Edukasi Elektro Journal of Tropical Soils ELINVO (Electronics, Informatics, and Vocational Education) JURNAL EKONOMI PENDIDIKAN DAN KEWIRAUSAHAAN Asian Journal of Agriculture Journal of Vocational Education Studies (JOVES) Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Ideguru: Jurnal Karya Ilmiah Guru International Journal of Engineering, Science and Information Technology Jurnal Wana Tropika Journal of Vocational and Technical Education (JVTE) Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Pendidikan Progresif Jurnal Wana Tropika Journal of Vocational and Career Education
Putu Sudira
Fakultas Teknologi Pertanian Universitas Gadjah Mada, Yogyakarta
Agriculture, Biological Sciences & Forestry 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 Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Social Sciences Transportation Other

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Optimization of Image Compression Using K-Means Clustering for Digital Heritage Archives Sahria, Yoga; Sudira, Putu; 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.
Application of Singular Value Decomposition for Image Compression of Yogyakarta Cosmological Axis in Digital Learning in Vocational Education Sahria, Yoga; Sudira, Putu; Salim, Mohamad Hidir Mhd
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
Leadership Strategy of the School Principal in Improving the Quality of Education in Remote Areas at SMKN 12 Sarolangun Berty, Yukinta Nasya; Sudira, Putu; Alim, Maulina Idami
Journal of Vocational Education Studies Vol. 9 No. 1 (2026): Vol 9 No 1 (IN PRESS)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/joves.v9i1.12127

Abstract

This study aims to analyze the leadership strategy of the school principal in improving the quality of education in remote areas, specifically at SMKN 12 Sarolangun. Remote areas often face various challenges in accessing quality education, such as limited infrastructure, minimal government support, and low community participation in education. Therefore, the role of the school principal is crucial in creating an environment that supports the improvement of education quality in these areas. This research employs a qualitative approach, with data collection techniques including in-depth interviews, observation, and documentation. Participants in this study include the school principal, teachers, and other relevant stakeholders involved in the educational process at SMKN 12 Sarolangun. The collected data were analyzed using thematic analysis techniques to identify and describe the various leadership strategies implemented by the principal in addressing the existing challenges and efforts to enhance the quality of education. The results show that the principal at SMKN 12 Sarolangun applies several leadership strategies, including human resource development, improving the quality of teaching and learning, and utilizing technology and networking with external parties. In addition, the principal is also active in building partnerships with the community and related agencies to support educational activities at the school. This research is expected to make a significant contribution to the development of educational leadership strategies in remote areas and serve as a reference for other school principals in efforts to improve education quality in similar conditions.
AI-Based Teacher Guidance in Vocational Schools:A Systematic Review on Generative AI for Holistic Student Development and Administrative Support Dymyati, Ria; Siswantoyo , Siswantoyo; Sudira, Putu; Fadlullah, Yanuar Agung; Maulana, Muhamad Riyan; Yusuf, Arya; Syahria, Yoga
Journal of Vocational and Career Education Vol. 11 No. 2 (2026): December 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jvce.v11i2.45441

Abstract

Generative artificial intelligence (GenAI) has emerged as a revolutionary technology in education, yet little is known about how GenAI can be specifically utilized to support teacher guidance and holistic student development, particularly in the context of vocational education. Evidence on how GenAI technology can enhance holistic student development in vocational schools, reduce administrative burdens, and support teacher guidance systems is explored in this systematic review. A synthesis of 45 peer-reviewed articles from the Scopus, Web of Science, and ERIC databases, published between 2020 and 2025 in accordance with PRISMA-ScR guidelines, was conducted. Five main thematic areas were identified through thematic analysis: (1) GenAI's function as a pedagogical assistant for individualized teacher support; (2) administrative automation that reduces teacher workload by thirty to forty percent; (3) natural language processing for qualitative analysis of student data; (4) comprehensive student development through career and character guidance; and (5) implementation challenges, including ethical issues, digital literacy gaps, and institutional readiness. These findings highlight GenAI's significant potential in addressing the teacher workload crisis, with approximately 40% of teachers' time spent on administrative tasks, while improving the quality of student guidance. However, its implementation still depends on resolving equity issues, developing robust institutional policies, and adopting human-centered pedagogical approaches. This review provides valuable insights for education practitioners, policymakers, and researchers seeking to implement sustainable AI-based guidance systems in vocational education. Recommendations include structured professional development programs, ethical frameworks for GenAI usage, and context-specific adaptation models for vocational schools, particularly in developing educational contexts.
Co-Authors Aanggraini, Yussi Abdulloh Hamid Abdurrahman, Afif Ade Dwi Putra Janata Adkha, Nur Faizal Agung B. Supangat Alim, Maulina Idami Anggraini, Yussi Areisy, Hafiidhiya Janata Azwar Maas Bambang Djadmo Kertonegoro Benito Heru Purwanto Berty, Yukinta Nasya Budiadi Choirunnisya', Lestari Rochmatun Dewi, Ayuana Kartika Didik Indradewa Dymyati, Ria Ernawati, Enik Erny Poedjirahajoe Fadlullah, Yanuar Agung HAQ, RIJALUL Haryono Supriyo Hidayati, Tuti I WAYAN WINDIA Iskandar, Ranu Jaya, Daniel Jesayanto Kumalajati, Erlynda Lilik Sutiarso Linda Setiawati, Linda Maulana, Muhamad Riyan Mochamad Bruri Triyono Muhammad Irfan Luthfi MULYONO, NITISAPTO Mutohhari, Farid Nikodemus P.P.E. Nainiti, Nikodemus P.P.E. Nining Wahyuningrum Nopriyanti Nopriyanti Nurdianah, Evi Nurdiansyah, Ferizal Nuryadin Eko Raharjo Nuryake Fajaryati Nuur Wachid Abdul Majid Pipit Utami Pramu Pramu Priyanto Purwantono, Anung Slamet Dwi Rinawan, Rangga Bayu Sabarnudin, Sambas Sahid Susanto Salim, Mohamad Hidir Mhd Siswantoyo , Siswantoyo Siti Mazizatuz Zahroh Siti Nurul Aini, Siti Nurul Sumarni, Titi Sumiyati, S Sutarto Sutarto Syahria, Yoga Thomas Sukardi Umi Rochayati Wakijah, Siti Wulandari, Ely Tri Yoga Sahria Yowanita Dwi Irwanti Yuanto, Teguh Arik Yusuf, Arya Zamroni, Z.