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Linear Algebra in Geometric Transformations Raihan Faisal Dzikra; Diny Syarifah
Jurnal Ilmiah Informatika dan Komputer Vol. 2 No. 1 (2025): June 2025
Publisher : CV.RIZANIA MEDIA PRATAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69533/h6b9sx39

Abstract

This study aims to deeply examine the role of Linear Algebra in geometric transformation through a combined qualitative-quantitative approach. By combining systematic literature studies, content analysis, GeoGebra-based virtual laboratory experiments, and field case studies, this study found that transformation matrices, vector spaces, and orthogonal projections are the main foundations in representing and manipulating geometric objects precisely. The results show that a deep understanding of Linear Algebra improves students' spatial visualization abilities by 34% and the efficiency of Augmented Reality (AR) application design by up to 27%.Startup NextVision AR Lab (Jl. Gegerkalong Hilir, Bandung) – cloud-based AR specialist for manufacturing & education. Problem Rendering high-detail 3D objects (>100k vertices) on a Snapdragon XR2 headset frequently drops frames. Hypothesis Optimizing the matrix transformation algorithms (model-view-projection, normal, pose) can reduce GPU load by up to 30% without compromising visual fidelity.
Matrix: Basic Concepts And Practical Applications In Daily Life Muhamad Dimas Aprijal; Diny Syarifah
Jurnal Ilmiah Informatika dan Komputer Vol. 2 No. 1 (2025): June 2025
Publisher : CV.RIZANIA MEDIA PRATAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69533/v728gt48

Abstract

Matrix, as a fundamental mathematical concept, have crucial applications in computer science. This study analyzes: the basic structure of matrices (definition, types, addition-multiplication-inverse operations), and their implementation in computational fields, including: digital image processing (pixel representation, image transformation), intelligent systems (matrix-based neural networks), data security (matrix encryption), and network optimization (routing algorithms). A combination of literature review and case analysis reveals that understanding matrices is the backbone of modern computing, particularly in the development of machine learning and computer vision. Findings indicate a gap between conventional matrix theory and the demands of large-scale computing in industry. This study recommends integrating examples of computer science applications into matrix education to prepare digital talent.
Co-Authors Muhamad Dimas Aprijal Raihan Faisal Dzikra