Application of Singular Value Decomposition for Image Compression of Yogyakarta Cosmological Axis in Digital Learning in Vocational Education

Research demonstrates that Singular Value Decomposition (SVD) is an effective method for compressing high-resolution images of the Yogyakarta Cosmological Axis without significantly degrading visual quality. The study found that most critical visual information can be retained using only a small number of dominant singular values, allowing for substantial file size reduction while preserving key details like object contours and structural characteristics. This enables efficient storage and transmission in digital learning systems, supporting vocational education with high-quality yet compact visual media. The approach balances compression ratio with image fidelity, as measured by Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Compression Ratio (CR). These findings validate SVD as a robust tool for developing contextually relevant digital learning resources.

Sahria, Y., Sudira, P., & Salim, M. H. M. (2026). Application of Singular Value Decomposition for Image Compression of Yogyakarta Cosmological Axis in Digital Learning in Vocational Education. International Journal of Engineering, Science and Information Technology, 6(1), 11–22. https://doi.org/10.52088/ijesty.v6i1.1732