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Abdul Aziz
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INDONESIA
The Eastasouth Journal of Information System and Computer Science
Published by Eastasouth Institute
ISSN : 30266041     EISSN : 3025566X     DOI : https://doi.org/10.58812/esiscs
Core Subject : Science,
ESISCS - The Eastasouth Journal of Information System and Computer Science is a peer-reviewed journal and open access three times a year (April, August, December) published by Eastasouth Institute. ESISCS aims to publish articles in the field of Enterprise systems and applications, Database management systems, Decision support systems, Knowledge management systems, E-commerce and e-business systems, Business intelligence and analytics, Information system security and privacy, Human-computer interaction, Algorithms and data structures, Artificial intelligence and machine learning, Computer vision and image processing, Computer networks and communications, Distributed and parallel computing, Software engineering and development, Information retrieval and web mining, Cloud computing and big data. ESISCS accepts manuscripts of both quantitative and qualitative research. ESISCS publishes papers: 1) review papers, 2) basic research papers, and 3) case study papers. ESISCS has been indexed in, Crossref, and others indexing. All submissions should be formatted in accordance with ESISCS template and through Open Journal System (OJS) only.
Articles 81 Documents
AI-Powered Quality Assurance and MIS Analytics: Building Resilient and Intelligent Digital Economies Sarker, Shakila; Nihat, Mashur Bin Mahmud
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 02 (2025): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v3i02.767

Abstract

Artificial intelligence (AI), predictive analytics, and management information systems (MIS) are all converging to remake U.S. companies into smart, adaptive ecosystems that can sustain economic resilience, cybersecurity, and software quality assurance (QA). This study synthesizes the empirical and conceptual findings of 20 peer-reviewed articles published between 2023 and 2025 to establish an integrated AI–MIS–QA Resilience Framework (AMQRF) that synthesizes automation, analytics, and governance in critical sectors such as IT, health, energy, and supply-chain infrastructure. The meta-synthesis reveals predictive QA with AI reduces software defect rates by 25–45%, MIS-based analytics increase operational visibility levels by 30–35%, and AI-driven cybersecurity models improve the accuracy of threat detection by up to 40%. All combined these flips enterprise resilience as an enterprise function of interconnected digital smartness and organizational learning. The study concludes by recommending a governance-aware architecture in which predictive QA, business analytics, and MIS co-evolve to facilitate sustainable competitiveness and national digital security.