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Journal : journal of deep learning computer vision and digital image processing

Performance and Security Analysis of Academic Information System Integration with Cloud Computing Technology Muhammad Ridho Ardiansyah; Mahmud; Ibnu Aqil
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 4 Issue 1 March 2026
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v4i1.978

Abstract

Purpose – The implementation of cloud computing technology for integrating Academic Information Systems (SIAKAD) offers solutions to challenges regarding centralized infrastructure, limited scalability, and data security issues faced by higher education institutions. This study aims to analyze and evaluate the performance and data security aspects of academic information systems integrated via cloud computing platforms.Methods – Employing both quantitative and qualitative approaches, the performance evaluation focuses on access speed, scalability, and operational efficiency. Meanwhile, the security analysis examines data protection mechanisms both at rest and in transit as well as compliance with cloud security standards.Findings – The results indicate that cloud-based integration significantly enhances access speed, service availability, and the flexibility of academic data management.Research implications – However, the findings also identify that data security and privacy governance remain critical challenges, necessitating the implementation of rigorous access controls such as encryption and identity management—to mitigate cyber threats.Originality – In conclusion, the adoption of cloud technology yields substantial efficiency improvements in academic services, provided it is accompanied by a comprehensive and structured cybersecurity strategy.