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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,
Computer Science & IT
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.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 17 Documents
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Search results for , issue "Vol. 2 No. 03 (2025): The Eastasouth Journal of Information System and Computer Science (ESISCS)" : 17 Documents clear
Optimizing CI/CD Pipelines for Multi-Cloud Environments: Strategies for AWS and Azure Integration Koneru, Naga Murali Krishna
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 03 (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.v2i03.534

Abstract

CI/CD pipelines are essential for modern software development to speed up application delivery and ensure reliability. However, organizations experience considerable management difficulties when they operate Continuous Integration and Deployment workflows between AWS and Azure. The research discusses multiple approaches to optimizing CI/CD pipelines and demonstrates their integration between AWS and Azure systems. The complete implementation guidelines within the method include selecting tools and best practices along with the necessary architectural elements to construct secure, scalable, and successful CI/CD pipelines. Success in deployment requires using standardized CI/CD platforms, infrastructure code implementation, and security platforms spanning multiple cloud environments, price reduction technologies, and consolidated monitoring tools. The deployment process framework integrates cloud platforms by implementing a solution that merges interoperability and security management alongside cost control functions. This proposal demonstrates its worth by applying the example project to prove significant benefits: fast deployment speed, lower costs, and dependable system infrastructure. The document explores actual applications of optimized pipelines, which decrease operational complexity and enhance resource utilization efficiency. The report includes deployment guidelines supplemented by practical examples to guide organizations during their adoption phase. Standardized CI/CD management approaches allow organizations to simplify deployment pipelines and automate workflow processes during multi-cloud connectivity risk management. The surveyed findings regarding optimizing AWS and Azure CI/CD workflows enable organizations to improve their DevOps performance in complex cloud infrastructure.
Smart, Safe, and Strategic: Transforming HR Data into Actionable Insights Without Compromising Security Singh, Kuwarpreet
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 03 (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.v2i03.537

Abstract

In today’s digital age, healthcare organizations are starting to use Human Resource (HR) data to make informed workforce decisions, enhance staffing, promote employee well-being, and navigate the ever-evolving regulatory landscape. This shift is largely due to well-built analytics tools found in platforms like Workday, which provide HR leaders with real-time insights into key metrics such as turnover rates, performance trends, and skills gaps. However, with all these benefits come serious risks. HR data in healthcare often includes very private information like health records, salaries, and personal details. If this information falls into the wrong hands, it can cause legal problems, damage the organization’s reputation, and hurt employees. Because of this, it's not enough to just use data well—it must also be protected at every stage. This paper explores how healthcare institutions can effectively and safely transform HR data into actionable insights through advanced analytics, all while prioritizing data privacy and compliance. It examines modern encryption techniques, privacy-preserving machine learning, and data governance frameworks that empower HR teams to achieve better outcomes securely. By reviewing case studies, peer-reviewed research, and industry best practices, this paper sheds light on the challenges, solutions, and emerging trends that will define the future of secure, data-driven HR ecosystems in healthcare.
Evaluating AI Responses: A Step-by-Step Approach for Test Automation Ramachandran, Sooraj
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 03 (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.v2i03.540

Abstract

Artificial Intelligence (AI) applications are transforming business operations, yet ensuring the accuracy, relevance, and reliability of AI-generated responses remains a critical challenge. This paper explores various methodologies for AI response evaluation, progressing from basic string comparisons to machine learning (ML)-based assessments and advanced Retrieval-Augmented Generation (RAG) techniques. We examine the advantages and limitations of each approach, illustrating their applicability with C# implementations. Our findings suggest that while traditional methods like fuzzy matching provide quick validation, ML-based and RAG-based approaches offer superior contextual understanding and accuracy. The study highlights the importance of automated evaluation pipelines for AI systems and discusses future research directions in improving AI response testing methodologies.
Brain-Computer Interfaces in Assistive Technologies: A Bibliometric Review Judijanto, Loso; Vandika, Arnes Yuli; Toalib, Ramli
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 03 (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.v2i03.547

Abstract

This study presents a comprehensive bibliometric review of scholarly literature on Brain-Computer Interfaces (BCIs) in assistive technologies, aiming to map research trends, intellectual structure, and collaborative patterns from 2000 to 2024. Using data retrieved from the Scopus database and analyzed through VOSviewer, this review identifies key contributors, institutional affiliations, and country-level collaborations. Results show a steady increase in publication output, with a sharp surge in 2024, indicating growing academic and clinical interest in BCI-assisted systems. The United States, Germany, and India emerge as the most productive countries, while institutions such as Eberhard Karls Universität Tübingen and Harvard Medical School lead in scholarly output. Author co-authorship analysis reveals influential figures and collaborative hubs, particularly in Europe and North America. Thematic clustering of keywords uncovers major research domains, including neurophysiological signal processing, machine learning applications, robotic control systems, and user-focused communication aids. Overlay and density visualizations suggest an evolution from foundational EEG-based research to more sophisticated, AI-enhanced and ethically grounded assistive technologies. This review provides a data-driven understanding of the field’s development and highlights future directions toward more inclusive, adaptive, and scalable BCI solutions for individuals with disabilities.
Research Advancements in Digital Twin Technology for Smart Manufacturing Judijanto, Loso; Vandika, Arnes Yuli
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 03 (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.v2i03.548

Abstract

This study presents a comprehensive bibliometric analysis of research developments in Digital Twin (DT) technology within the domain of smart manufacturing. Drawing on Scopus-indexed publications from 2010 to 2024, the study explores the growth patterns, thematic structures, institutional contributions, collaborative networks, and emerging research trends using VOSviewer. The findings reveal a sharp increase in publication volume, particularly in 2024, indicating growing academic and industrial interest. China dominates the research landscape in terms of both institutional productivity and international collaboration, followed by India and the United States. Keyword co-occurrence analysis identifies “smart manufacturing,” “digital twin,” and “industry 4.0” as core themes, with increasing emphasis on artificial intelligence, optimization, collaborative robots, and Industry 5.0 in recent years. Co-authorship and country collaboration maps illustrate dense scholarly networks centered around prominent authors and regions. Despite significant progress, the study identifies gaps in real-world implementation, standardization, and ethical considerations. These insights offer valuable direction for future interdisciplinary research and policy strategies aimed at integrating DT technologies into next-generation manufacturing ecosystems.
Emerging Research Trends in Natural Language Processing for Multilingual AI Judijanto, Loso; Vandika, Arnes Yuli
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 03 (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.v2i03.549

Abstract

This study explores the emerging trends and developments in Natural Language Processing (NLP) for Multilingual Artificial Intelligence (AI) through a comprehensive bibliometric analysis. Drawing on data from the Scopus database spanning 2013 to 2023, the research identifies key publication patterns, influential contributors, thematic clusters, and collaboration networks that shape the evolution of multilingual NLP. The analysis reveals a significant increase in research activity over the past five years, particularly driven by advancements in deep learning and the emergence of multilingual pretrained models such as mBERT and XLM-RoBERTa. Institutions from the United States, India, and China lead the global research landscape, while collaborative clusters highlight the interdisciplinary and international nature of the field. Keyword analysis shows a paradigm shift from rule-based and statistical approaches to neural and transformer-based architectures, with increasing application in healthcare, social media, and big data environments. Despite this growth, the study identifies ongoing challenges, including disparities in language representation, bias in model training, and the need for ethical and inclusive research practices. The findings provide a strategic overview for researchers, policymakers, and practitioners aiming to advance equitable and effective multilingual AI systems.
Evaluating the Effectiveness of APM Tools (Dynatrace, AppDynamics) in Real-Time Performance Monitoring Ramdoss, Vasudevan Senathi; Rajan, Priya Darshini Mukunthu
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 03 (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.v2i03.593

Abstract

Application Performance Monitoring (APM) tools play a crucial role in ensuring optimal performance and reliability of cloud-based applications. Dynatrace and AppDynamics are two leading APM solutions that provide real-time monitoring, diagnostics, and performance optimization. This paper evaluates their effectiveness in real-time performance monitoring by analyzing their capabilities, impact on system performance, and ability to detect and resolve performance issues.

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