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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 80 Documents
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SINERGISTA (Agrotourism Synergy): A Sustainable Tourism Development Strategy Based on Digitalization Through the Pentahelix Collaboration Model to Support the 2030 SDGs Ramadhan, Dede; Ahleyani, M.; Lestari, I Gusti Ayu Cintiya Widya; Ramadhan, Hafiz Okta; Yolanda, Steviani
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 01 (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.v3i01.687

Abstract

Sustainable tourism is a key pillar in achieving the Sustainable Development Goals (SDGs) 2030. This study introduces SINERGISTA (Sinergi Agrowisata) as a digital-based sustainable tourism development strategy through a Pentahelix Connectors collaboration model involving government, academia, business actors, communities, and media. The objective of this research is to formulate an implementable strategy for digital-based agrotourism development aimed at enhancing economic, social, environmental, and cultural value in a sustainable manner. This study employs a descriptive-exploratory approach through in-depth interviews, field observations, and documentation studies on leading agrotourism destinations in Indonesia. Findings reveal that integrating digitalization into marketing platforms, reservation systems, and virtual reality promotions expands market reach, increases tourist engagement, and facilitates transparent destination management. Moreover, the Pentahelix Connectors collaboration is proven effective in reinforcing synergy among stakeholders in promoting service innovation, improving human resource capacity, and fostering participatory environmental management. Strategic recommendations include strengthening digital literacy among agrotourism actors, ensuring regional government policy support, and providing inclusive and adaptive technological infrastructure.
Bibliometric Mapping of Extended Reality (XR) in Education and Business Domains Judijanto, Loso
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 01 (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.v3i01.715

Abstract

This study presents a comprehensive bibliometric mapping of Extended Reality (XR) research in education and business domains, providing insights into thematic trends, collaborative networks, and emerging areas of interest. Using the Scopus database as the primary data source, publications from 2010 to 2025 were retrieved and analyzed exclusively with VOSviewer to generate keyword co-occurrence, author collaboration, country collaboration, temporal evolution, and density visualizations. The results reveal that virtual reality, augmented reality, extended reality, and engineering education are the most prominent and interconnected themes, with newer research increasingly focusing on metaverse, artificial intelligence, and Industry 4.0 integration. Co-authorship and country network maps show that the United States, Italy, Germany, and India are central hubs driving global research partnerships, while author collaborations cluster into specialized yet interconnected groups. The study highlights how education-oriented XR research emphasizes immersive learning, e-learning, and gamification, while business-focused studies target marketing innovation, customer engagement, and sustainability. These findings contribute to a holistic understanding of XR’s interdisciplinary evolution, offering strategic guidance for researchers, educators, industry leaders, and policymakers to foster innovation and cross-domain integration.
Research Trends in Cyber-Physical Systems within Information Systems: A Bibliometric Approach Judijanto, Loso; Baharuddin, Baharuddin
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 01 (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.v3i01.716

Abstract

This study presents a comprehensive bibliometric analysis of research trends in Cyber-Physical Systems (CPS) within the Information Systems (IS) domain, utilizing data from the Scopus database (2010–2025) and analyzed using VOSviewer. By examining keyword co-occurrence, overlay mapping, density visualization, and collaboration networks, the study identifies the thematic structure, intellectual linkages, and global research collaborations shaping the CPS–IS landscape. Results reveal that CPS research is highly interdisciplinary, with central themes including internet of things, network security, machine learning, and information management, while emerging areas such as digital twin, intrusion detection, and false data injection attacks are gaining momentum. The overlay visualization indicates a recent shift toward AI-driven security and optimization in CPS applications. Authorship and country collaboration analyses highlight China, the United States, Germany, and India as leading contributors with extensive international linkages. This study contributes to the theoretical understanding of CPS as an integrated socio-technical construct in IS and provides practical insights for aligning research, policy, and industrial strategies with evolving technological priorities in the era of Industry 4.0.
Research Trends in Smart Workflow Automation: A Bibliometric Study from Scopus Judijanto, Loso; Qadriah, Laila
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 01 (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.v3i01.717

Abstract

This study presents a comprehensive bibliometric analysis of smart workflow automation research using Scopus-indexed publications to map its intellectual structure, thematic evolution, and global collaboration patterns. A dataset covering the period 2000–2025 was extracted, cleaned, and analyzed using VOSviewer for science mapping and Microsoft Excel for performance metrics. Results reveal that the field is anchored by core concepts such as automation, internet of things, machine learning, and work-flows, which consistently occupy central positions in the research network. Emerging themes, including blockchain, supply chains, security, and ubiquitous computing, reflect a shift toward secure, interconnected, and efficiency-driven automation ecosystems. Co-authorship and country collaboration analyses highlight the pivotal roles of the United States, China, and Germany, supported by extensive cross-border partnerships. Temporal trend mapping demonstrates a transition from early industrial automation and foundational cyber-physical infrastructure toward integrated, value-oriented applications. The study offers practical guidance for industry adoption strategies, enriches theoretical understanding of the field’s conceptual landscape, and identifies promising research frontiers for future exploration.
The Role of Federated Learning in Enhancing Data Privacy in Distributed Environments in Indonesia ZM, Ajub Ajulian; Sinuraya, Enda Wista; Winardi, Bambang
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 01 (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.v3i01.720

Abstract

This study investigates the role of federated learning (FL) in enhancing data privacy within distributed environments in Indonesia. With the increasing reliance on digital technologies, data privacy has become a critical concern, particularly in decentralized systems. A quantitative research approach was employed, involving 130 respondents from various professional backgrounds engaged with distributed data systems. Data were collected using a structured questionnaire with a five-point Likert scale (1–5) and analyzed using SPSS version 25. Descriptive, correlation, and regression analyses revealed that federated learning significantly contributes to improving confidentiality, trust, and compliance in distributed environments. The results indicate that FL not only safeguards sensitive data but also enhances stakeholder confidence and supports adherence to regulatory standards, such as Indonesia’s UU PDP. The study provides empirical evidence supporting the adoption of federated learning as a privacy-preserving technology and offers practical insights for organizations and policymakers seeking secure and compliant digital ecosystems.
Analysis of the Trends in AI-as-a-Service (AIaaS) Implementation in Technology Companies in Indonesia Sinuraya, Enda Wista; ZM, Ajub Ajulian; Winardi, Bambang
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 01 (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.v3i01.721

Abstract

This study examines the impact of adoption drivers, perceived benefits, and implementation challenges of Artificial Intelligence-as-a-Service (AIaaS) on the organizational performance of technology companies in Indonesia. Using a quantitative approach, data were collected from 200 respondents through structured questionnaires and analyzed with multiple regression techniques. The results reveal that adoption drivers and perceived benefits have a significant positive influence on organizational performance, while implementation challenges exert a negative impact. These findings suggest that while AIaaS offers considerable potential for efficiency, innovation, and competitive advantage, unresolved barriers such as data security, system integration, and limited human resources hinder its effectiveness. This study contributes to the growing body of literature on AI adoption in emerging economies by highlighting the dual role of enablers and inhibitors in shaping AIaaS outcomes. Practical implications include the need for organizations to invest in digital skills development and risk management, and for policymakers to establish clear regulatory frameworks to accelerate responsible AIaaS adoption in Indonesia.
The Process of Message Production Among Whatsapp Social Media Users Pratikto, Riyodina G.; Kholil, Kholil; Widaningsih, Titi; Jamalullail, Jamalullail
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 01 (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.v3i01.739

Abstract

As a result of developments in technology and information, the way individuals communicate has changed. This is also the case with the SMAN 3 Bandung Class of '82 Alumni Association, which uses a WhatsApp group (WAG) as a platform to gather and exchange information. However, there is a problem, namely that the dynamics of communication within the ikasma3badg82 WAG cannot always be conveyed in their entirety. The researcher argues that dishonesty in conveying messages within the WAG is part of an effort to maintain solidarity among group members. The objectives of this study include (1) to determine the message production process within the ikasma3bdg82 WAG and (2) to determine how the message production process within the ikasma3bdg'82 WAG is related to the characteristics of its members. The research methodology uses a constructivist paradigm and a qualitative approach. The results of the study include (1) The message production process carried out by members of the ikasma3bdg82 WAG, between passive and active members, is fairly balanced. (2) Based on Trait Theory, members of the ikasma3bdg82 WAG can be classified into Conversational Narcissism, Argumentativeness, and Social Communication Anxiety. The uniqueness of this study is the emergence of the Super Conversational Narcissism component, which is a development of Littlejohn's Trait Theory.
Resilient Intelligence: AI and MIS in the Cyber-Economic Era Ahsan, Rezwan Moin; Uddin, Borhan; Hossen, Tawhid; Das, Sachin
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.v3i02.758

Abstract

The integration of artificial intelligence (AI) with management information systems (MIS) has transformed how countries protect their digital infrastructure, oversee organizational projects, and maintain economic resilience. This study consolidates recent developments in cybersecurity, project governance, software quality assurance (QA), energy analytics, and economic intelligence to propose an integrated model, AI-for-MIS Cyber-Energy-Economic Resilience (AM-CEER), that improves proactive defense, predictive governance, and sustainable performance. This research synthesizes over seventy recent peer-reviewed works, incorporating deep learning models (LSTM, Transformer), federated analytics, explainable AI (XAI), and cloud-based MIS infrastructures into a cohesive framework. Research demonstrates that AI-enhanced MIS infrastructures enhance cyber threat detection accuracy by more than 30%, diminish IT project risk exposure by 25%, and elevate predictive capability for energy and economic systems by around 40%. The proposed AM-CEER architecture creates a framework for digital governance that integrates data-driven decision-making with cybersecurity, quality assurance automation, and macroeconomic forecasting, thereby ensuring the long-term stability of essential national services.
Explainable AI Framework for Precision Public Health in Metabolic Disorders: A Federated, Multi-Modal Predictive Modelling Approach for Early Detection and Intervention of Type 2 Diabetes Rahman, Md Habibur; Khan, Md Nazibullah; Das, Sachin; Uddin, Borhan
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.v3i02.759

Abstract

One of the biggest public health problems of the twenty-first century is metabolic disorders, especially Type 2 diabetes (T2D). Morbidity, mortality, and medical expenses can be significantly decreased by early detection of at-risk people. However, nonlinear, multi-factorial, and high-dimensional interactions that influence the development of disease are not well captured by traditional risk-scoring methods. In order to predict and interpret the risk of type 2 diabetes and related metabolic disorders, this study creates an Explainable AI (XAI) framework for precision public health that combines multi-modal data, such as genomic profiles, lifestyle factors, socioeconomic determinants, and electronic health records (EHR). We create a federated, hybrid model that combines Random Forest classifiers, Deep Neural Networks (DNN), and Gradient Boosting Machines (LightGBM/XGBoost), building on federated and ensemble learning paradigms. Shapley Additive Explanations (SHAP) and counterfactual analysis are used to uncover personalized, actionable risk profiles in order to attain explainability. Harmonized multi-institutional datasets with over 200,000 records gathered from several U.S. health systems are used to train the model. The results show a calibrated Brier score of 0.12, sensitivity of 89%, specificity of 87%, and AUC of 0.93 ± 0.01. The socioeconomic deprivation index, polygenic risk score, BMI slope, and HbA1c trajectory are the main factors, according to SHAP study. Federated deployment protects data privacy while preserving performance. These results show that federated, explainable AI pipelines can facilitate population-based, privacy-preserving, andThe goal of precision public health is being advanced by large-scale early-warning systems for managing metabolic diseases.
Cognitive Cyber Defense: AI–MIS Integration through Big Data and Cloud Frameworks for Next-Generation Digital Resilience Hossain, Md Delwar; Sikder, Mohammad Somon; Uddin, Md Salah; Ahsan, Rezwan Moin; Uddin, Borhan; Hossen, Tawhid
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.v1i02.764

Abstract

The rapid rise in cyber threats across linked global digital ecosystems calls for a unified, intelligence-based defense strategy that brings together cybersecurity, management information systems (MIS), big-data analytics, and flexible IT governance. This study builds on the work of Kaur et al. (2023), Hasan et al. (2023), Mahmud et al. (2023), and Das et al. (2023) to create a comprehensive framework that uses artificial intelligence (AI), cloud computing, and data-driven decision-making to make digital systems more resilient. The research formulates an integrated AI–MIS Cyber-Defense Framework via a meta-synthesis of present empirical studies, clarifying the interaction among machine-learning analytics, predictive threat intelligence, and adaptive governance feedback loops. These interdependencies together improve the accuracy of detection, the ability to understand the issue in context, and the ability of organizations to adjust in unstable cyber environments. Quantitative evaluation shows that the system works better than traditional control systems. The average detection area under the curve (AUC) is over 0.93, the precision–recall metrics are above 0.90, and the composite resilience index is 27 percent higher. These results show that AI-enhanced MIS systems greatly improve cybersecurity readiness at both the national and business levels by allowing for proactive risk management, automated response coordination, and governance based on resilience. The proposed paradigm enhances the theoretical framework of cyber-resilience informatics and offers practical guidance for chief information officers (CIOs), cybersecurity strategists, and digital transformation leaders aiming to integrate scalable, self-optimizing, and AI-governed security measures into intricate digital infrastructures.

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