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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 122 Documents
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AI-Driven Cyber Threat Intelligence as a Management Information System: Integrating Cybersecurity Governance and IT Project Management for Organizational Resilience Orthi, Shuchona Malek; Chakraborty, Partha; Siam, Md Abubokor; Shan-A-Alahi, Ahmed; Al Zaiem, Abdullah; Hasan, Syed Nazmul; Kaur, Jobanpreet; Mahmud, Foysal; Goffer, Mohammad Abdul
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): 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.v1i02.873

Abstract

As organizations quickly become increasingly digital, they are experiencing escalating, complex cyber threats. These challenges make cybersecurity an important area for managers and governance, and not just a technical problem. Even if you are a heavy investor in security tech, it still happens to many companies to suffer from cyber. The reasons are broken information flows, a lack of clear visibility among managers, and misalignment between the security operations and the overall decision-making. This research rethinks Artificial Intelligence (AI) based Cyber Threat Intelligence (CTI) as a Management Information System (MIS). It combines the approach of cybersecurity governance and IT project management to increase the resilience of organizations. Using MIS theory, cybersecurity governance models, and studies of IT project management, the paper derives one cohesive model for translating raw threat data into useful managerial insight. Through a design science methodology, the research chooses a lot of scholarly sources and demonstrates a layered AI-enabled CTI-MIS architecture. This is good architecture for strategic oversight, risk-based governance, and flexible project execution. The paper extends the theory of MIS to Artificial Intelligence (AI) driven cybersecurity intelligence and provides practical knowledge for companies seeking to achieve resilient digital transformation in a time of evolving cyber threats.
Performance Optimization Strategies for High-Concurrency Spring Boot Microservices in Enterprise Financial Systems Singi, Kamalakar Reddy
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): 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.v1i02.883

Abstract

This paper investigates performance optimization strategies for Spring Boot–based microservices deployed in high-concurrency enterprise financial transaction systems. Although microservices improve modularity and scalability, financial workloads expose bottlenecks related to database contention, synchronous execution, and Java Virtual Machine (JVM) resource management. A coordinated, multi-layer performance optimization framework is proposed, addressing application-level, data-access-level, and runtime-level challenges. The framework is validated using a simulated high-concurrency financial transaction workload. Experimental results demonstrate improved response time, higher throughput, enhanced runtime stability, and reduced error rates under peak load conditions.
Cloud-Based Information Retrieval for Big Data: A Survey of Architectures and Scalability Challenge Chowdhury, Tanay
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 03 (2026): 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.v3i03.937

Abstract

Cloud computing has become a paradigm of managing, storing and retrieving large amounts of data emanating in contemporary digital applications. The mode of information retrieval (IR), which is typically insufficient in large-scale, heterogeneous, and dynamic data settings, has been severely challenged by the issue of big data, namely its high volume, high velocity, high diversity, high veracity, and high value. Cloud retrieval information systems take advantage of the elasticity, scalability and on-demand provisioning of cloud systems to facilitate effective and cost-effective access to data across distributed platforms. This work is a critical overview of the concept of big data and cloud-based IR, with a specific emphasis on the most significant models of cloud service, the specifics of data types, and the prospects of ML and DL to improve the quality of retrieval and relevance. Moreover, the paper logically examines key scalability issues, such as distributed storage management, index maintenance, query processing latency, load balancing and resource provisioning. All critical issues related to security and privacy, including leakage of data, insider threats, and vulnerability of programming interfaces, and multi-tenancy risks are also discussed. This paper, by summarizing the available literature and discovering gaps in the research, offers useful information on how scalable, secure, and intelligent information retrieval systems can be designed, as well as presents future research opportunities so as to facilitate reliable deployment of the system in data-intensive applications.
Semantic Search with Vector Database: A Comprehensive Review of Models, Indexing and Applications Chowdhury, Tanay
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 03 (2026): 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.v3i03.938

Abstract

The use of semantic search with the help of vector databases has become an impressive paradigm of retrieving the pertinent information by offering the contextual and conceptual sense of the information searching more than using the conventional methods of keyword searching. This paper provides an in-depth overview of the models of vector representation, transformer-based semantic encoders, and technologies of vectors database that jointly allow efficient and error-free semantic search. Classical distributional semantics, word-level embeddings, and transformer architectures are presented as background methods of making designed generating meaningful vectors representations. The paper also looks at the contemporary databases of vectors and indexing mechanisms which enable scalable similarity search in high-dimensional data. Moreover, different distance measures, hash algorithms and indexing strategies based on graphs are evaluated to determine how they can be used to maximize retrieval. Lastly, the paper presents practical examples of semantic searching with the use of the vector databases with text, image, audio and conversational applications, outlining both the main challenges and research opportunities.
Performance Evaluation of Machine Learning Inference Workloads in Containerized Cloud Computing Environments Konda, Manisha
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.v3i02.949

Abstract

Machine learning (ML) systems are increasingly deployed in cloud-native environments where scalability, portability, and resource efficiency are essential. There are many scenarios in which the Docker and Kubernetes containerization solution are the best solution for machine learning inferencing services as the application scales, moves, and seeks every efficiency. However, the performance of machine learning inferencing services within a containerized cloud environment still needs to be explored. What is the performance of machine learning inferencing services within a containerized cloud environment? The performance of machine learning inferencing services within a containerized cloud environment needs to be explored. The aim of the exploration is to understand the performance of various machine learning models within a containerized cloud environment and to determine the major factors affecting the performance of machine learning inferencing services. Several machine learning models are implemented using Python-based frameworks and deployed as microservices in Docker containers. The experiments are performed by sending simultaneous prediction requests from multiple users to the deployed models. The study establishes baseline benchmarks, which demonstrate the impact of containerization on inference speed and efficiency. This provides useful and practical knowledge for building scalable AI systems and establishes the foundation for future work, such as optimizing ML deployment pipelines, incorporating privacy-preserving inference techniques, and improving container orchestration for AI workloads.
Explainable Decision Support Systems (2010–2024): A Biblio-metric Review of Intellectual Structure Judijanto, Loso
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 03 (2026): 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.v3i03.992

Abstract

This study provides a comprehensive bibliometric analysis of Explainable Decision Support Systems (EDSS) research published between 2010 and 2024, with the objective of mapping its intellectual structure, thematic evolution, and emerging trends. Data were collected from the Scopus database and analyzed using quantitative bibliometric techniques, including performance analysis and science mapping, supported by visualization through VOSviewer. The results indicate a significant growth in EDSS publications, particularly after 2019, driven by the increasing demand for transparency and accountability in artificial intelligence-based decision-making. The network and density analyses reveal that core research themes are centered on machine learning, decision support systems, and artificial intelligence, while emerging topics include interpretability, trust, and human-centered design. Co-authorship and co-citation analyses highlight the interdisciplinary nature of the field, with strong contributions from domains such as healthcare, industry, and data science. Furthermore, the findings demonstrate a shift from purely technical model development toward the integration of explainability, ethics, and user interaction in decision-making systems. This study contributes by offering a structured overview of EDSS research and identifying key directions for future studies, particularly in developing standardized evaluation frameworks and expanding applications beyond dominant domains such as healthcare.
Federated Learning in Distributed Computing: A Scopus-Based Bibliometric Analysis of Research Trends Judijanto, Loso
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 03 (2026): 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.v3i03.993

Abstract

This study presents a comprehensive bibliometric analysis of federated learning within distributed computing, aiming to explore research trends, intellectual structures, and emerging themes in the field. Data were retrieved from the Scopus database covering publications from 2010 to 2025. The analysis employs performance metrics and science mapping techniques, including co-authorship, keyword co-occurrence, citation, and density visualization using VOSviewer. The results reveal a significant increase in research output, particularly after 2018, driven by the growing demand for privacy-preserving machine learning and the expansion of edge computing and Internet of Things (IoT) ecosystems. Key research clusters focus on data privacy, system optimization, distributed machine learning, and real-world applications such as healthcare and industrial systems. The findings also highlight strong global collaboration networks and the dominance of contributions from leading countries such as China and the United States. Furthermore, recent trends indicate a shift toward integrating federated learning with advanced technologies such as blockchain, reinforcement learning, and energy-efficient systems. This study provides a structured overview of the field and offers valuable insights for researchers and practitioners in identifying research gaps and future directions in federated learning within distributed computing.
Human–AI Interaction Research (2005–2025): A Bibliometric Mapping Based on Scopus Data Judijanto, Loso
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 03 (2026): 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.v3i03.994

Abstract

This study provides a comprehensive bibliometric analysis of Human–AI Interaction (HAI) research from 2005 to 2025 using data retrieved from the Scopus database. The objective is to map the intellectual structure, research trends, and thematic evolution of the field. A total of relevant publications were analyzed using bibliometric techniques, including co-authorship, co-citation, keyword co-occurrence, overlay, and density visualization, supported by VOSviewer. The results indicate a significant growth in HAI research, particularly after 2015, driven by rapid advancements in artificial intelligence technologies such as machine learning, deep learning, and large language models. The findings reveal that early research was predominantly technology-oriented, focusing on algorithm development and system performance, while more recent studies emphasize human-centered aspects such as trust, explainability, user experience, and ethical considerations. Network analysis shows that research collaboration is concentrated in developed regions, with strong contributions from North America, Europe, and East Asia, while participation from developing regions remains limited. Keyword analysis identifies major thematic clusters, including technical foundations, human-centered interaction, and application domains such as healthcare and decision-making. The study also highlights emerging topics such as generative AI and human–AI collaboration. Overall, this research provides a structured overview of the HAI research landscape and offers insights into future research directions, emphasizing the importance of interdisciplinary approaches and responsible AI development.
Knowledge Graphs in Information Systems: A Scopus Bibliometric Analysis of Research Evolution Judijanto, Loso
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 03 (2026): 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.v3i03.995

Abstract

This study explores the evolution of knowledge graph research within the field of information systems through a comprehensive bibliometric analysis. Data were collected from Scopus and analyzed using quantitative techniques, including performance analysis and science mapping. Visualization was conducted using VOSviewer to examine co-authorship networks, citation structures, and keyword co-occurrence patterns. The results indicate a significant growth in publications, particularly after 2012, reflecting the increasing importance of knowledge graphs in data-driven environments. Co-authorship analysis reveals strong global collaboration, with dominant contributions from countries such as China and the United States. Citation analysis highlights foundational studies in bioinformatics, semantic networks, and graph-based learning as key drivers of the field. Meanwhile, keyword analysis shows a clear thematic shift from ontology and information management toward artificial intelligence, machine learning, natural language processing, and recommender systems. The overlay and density visualizations further confirm the emergence of application-oriented and AI-integrated research trends. Overall, this study provides a structured overview of the intellectual landscape of knowledge graph research and identifies future directions, particularly in the integration of knowledge graphs with advanced artificial intelligence technologies.
An Analysis of the Role of Software Engineering in Improving the Quality of Academic Information Systems at Wirahusada University Medan Dompu Hot Asih, Dybio; Ardian Tarigan, Dede; Efranata Tarigan, Jona
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 03 (2026): 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.v3i03.888

Abstract

Software engineering plays a crucial role in ensuring the quality of information systems used in higher education institutions. This study aims to analyze the role of software engineering practices in improving the quality of academic information systems at Wirahusada University Medan. The research focuses on how systematic software engineering processes contribute to system reliability, usability, and maintainability. This study employs a descriptive research method with a case study approach. Data were collected through observations, interviews, and questionnaires involving system users and developers. The analysis results indicate that the application of software engineering principles, including requirements analysis, system design, implementation, and testing, has a significant impact on improving system quality. The findings show that structured development processes enhance system performance, reduce errors, and increase user satisfaction. Therefore, the implementation of proper software engineering practices is essential to ensure the quality and sustainability of academic information systems in higher education institutions.

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