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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 102 Documents
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LLM-Based Autonomous Remediation for DevSecOps Pipelines Kakarla, Roshan
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 02 (2024): 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.v2i02.856

Abstract

Modern DevSecOps pipelines operate at a scale and velocity that exceeds the cognitive and operational capacity of traditional rule-based automation and human-centric incident response. While monitoring, alerting, and security scanning tools have matured, remediation remains largely manual, fragmented, and reactive resulting in prolonged mean time to resolution (MTTR), configuration drift, and governance gaps. This paper proposes a novel LLM-Based Autonomous Remediation Framework (LLM-ARF) that introduces a risk-aware, policy-governed control plane for automated detection, diagnosis, and remediation across DevSecOps pipelines. Unlike existing approaches that rely on static runbooks or narrow AI classifiers, LLM-ARF integrates large language models as reasoning agents embedded within a constrained, auditable, and human-supervised execution loop. The framework explicitly separates cognition, decision authority, and actuation, enabling scalable autonomy while preserving accountability and compliance. We present the architectural design, lifecycle control flow, and governance mechanisms of LLM-ARF, and evaluate its operational impact using real-world DevOps metrics such as MTTR reduction, alert fatigue mitigation, and toil reduction. The results demonstrate that LLM-ARF enables a step-function improvement in remediation reliability without compromising safety or human oversight, positioning autonomous remediation as a viable next evolution of enterprise DevSecOps systems.
Bibliometric Mapping of Digital Transformation in Government Services Judijanto, Loso
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.v3i02.860

Abstract

The rapid advancement of digital technologies has fundamentally reshaped the way governments design, deliver, and manage public services, positioning digital transformation as a strategic priority in contemporary public administration. Alongside this transformation, scholarly interest in digital government services has expanded rapidly, resulting in a fragmented and diverse body of literature. This study aims to systematically map the intellectual structure, thematic evolution, and collaborative patterns of research on digital transformation in government services through a bibliometric approach. Using publication data retrieved from the Scopus database covering the period 2003–2023, this study employs VOSviewer to conduct keyword co-occurrence analysis, co-authorship analysis, institutional collaboration mapping, and country-level collaboration analysis. The findings reveal that digital transformation serves as the central integrative concept connecting governance reform, public service delivery, decision-making, and advanced technologies such as artificial intelligence. Temporal analysis indicates a clear shift from early technology-oriented themes toward service-centric, data-driven, and intelligent governance paradigms. Collaboration networks further demonstrate the globalization of digital government research, with strong contributions from North America, Europe, and rapidly growing participation from Asia and emerging economies. Overall, this study provides a comprehensive overview of the knowledge landscape, highlights emerging research frontiers, and offers insights to guide future academic inquiry and policy development in digital government services.
Improving Cybersecurity Resilience in Indonesian Cloud Infrastructures Through AI‑Based Threat Intelligence Simarangkir, Manase Sahat H
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.v3i02.861

Abstract

The rapid expansion of cloud computing adoption in Indonesia has significantly increased organizational exposure to cyber threats, making cybersecurity resilience a critical strategic priority. This study examines the role of Artificial Intelligence (AI)-based threat intelligence in enhancing cybersecurity resilience within Indonesian cloud infrastructure. Using a quantitative research design, data were collected from 155 respondents consisting of IT managers, cloud engineers, and cybersecurity practitioners through a structured Likert-scale questionnaire. The data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS 3). The results indicate that AI-based threat intelligence has a significant positive effect on threat detection accuracy and response effectiveness. Both threat detection accuracy and response effectiveness also have significant positive effects on cybersecurity resilience. Furthermore, AI-based threat intelligence directly strengthens cybersecurity resilience and indirectly enhances it through the mediation of threat detection accuracy and response effectiveness. These findings confirm that AI-driven cybersecurity systems play a strategic role in improving adaptive defense capabilities, accelerating incident response, and strengthening organizational resilience in cloud environments. This study provides important implications for policymakers, cloud service providers, and organizations in designing intelligent cybersecurity frameworks to support Indonesia’s sustainable digital transformation.
Mapping Blockchain Identity Management Research: A Bibliometric Analysis (2010–2025) Judijanto, Loso
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.v3i02.862

Abstract

This study presents a comprehensive bibliometric analysis of blockchain identity management research published between 2010 and 2025, aiming to map its intellectual structure, thematic evolution, and global collaboration patterns. Using data retrieved from the Scopus database and analyzed with VOSviewer, the study applies network visualization, overlay visualization, density mapping, citation analysis, and co-authorship analysis to uncover dominant research streams and emerging frontiers. The results reveal that the field is conceptually centered on blockchain-based authentication and decentralized identity management systems, with increasing scholarly attention toward privacy-preserving mechanisms such as zero-knowledge proofs, anonymity, and data protection. Thematic evolution indicates a clear transition from foundational infrastructure-oriented studies to application-driven and regulatory-sensitive research domains, including e-government, IoT, healthcare, and digital governance. Collaboration analysis highlights the leading role of China and India, supported by strong transcontinental linkages with the United States and European countries, reflecting a globally interconnected yet regionally concentrated research landscape. By systematically mapping publication trends, thematic clusters, and collaboration networks, this study provides a structured knowledge base that supports future theoretical development, guides practical implementation, and informs policy formulation in blockchain-based digital identity ecosystems.
Continuous Security Validation of Linux Systems Using Configuration-as-Code Alti, Balaramakrishna
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.v1i02.863

Abstract

Enterprise Linux systems form the foundation of critical business services across on-premises, hybrid, and cloud infrastructures. Maintaining a secure configuration posture over time remains a persistent challenge due to manual changes, emergency fixes, and inconsistent enforcement of security standards. Traditional security validation approaches rely on periodic audits and reactive assessments, which fail to detect configuration drift in a timely manner. This paper presents a continuous security validation approach for Linux systems using configuration-as-code principles. The proposed approach encodes security controls, compliance requirements, and system hardening rules as declarative configurations that are continuously evaluated against live system state. By integrating configuration-as-code with automated validation and remediation workflows, the approach enables near real-time detection of security deviations and consistent enforcement of approved baselines. A controlled experimental evaluation conducted on a representative Linux environment demonstrates improved security posture consistency, reduced configuration drift duration, and faster remediation compared to traditional audit-based validation methods. The results show that continuous security validation using configuration-as-code provides a scalable and auditable mechanism for maintaining secure Linux system configurations.
Systematic Enforcement of CIS-Aligned Security Controls for Kubernetes Worker Nodes Alti, Balaramakrishna
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 01 (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.v1i01.864

Abstract

Securing Kubernetes worker nodes remains a persistent challenge in enterprise environments due to configuration drift, inconsistent operating system hardening, and limited visibility into runtime security posture. While the Center for Internet Security (CIS) provides benchmark recommendations for Kubernetes and Linux systems, manual enforcement of these controls is error-prone and difficult to sustain at scale. This paper presents an automated approach for hardening Kubernetes worker nodes by integrating CIS benchmark compliance with Linux security controls using configuration management automation. The proposed framework focuses on repeatable enforcement, continuous compliance validation, and operational stability. We describe the system architecture, control mapping strategy, and automation workflow, and evaluate its impact on configuration compliance and operational availability in a controlled Kubernetes environment. Results demonstrate measurable improvements in benchmark compliance while maintaining cluster stability, highlighting the feasibility of automation-driven security hardening for Kubernetes worker nodes.
Accelerating AI and Data Strategy Transformation: Integrating Systems, Simplifying Financial Operations Integrating Company Systems to Accelerate Data Flow and Facilitate Real-Time Decision-Making Kusumba, Surender
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 02 (2024): 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.v2i02.866

Abstract

The rapid advancement of artificial intelligence (AI) and data-driven technologies has intensified the need for organizations to integrate heterogeneous systems and redesign their data strategies to support real-time decision-making and financial efficiency. This study investigates how system integration accelerates AI and data strategy transformation and simplifies financial operations in the Energy and Utilities sector in the United States. Using a quantitative research design, data were collected from 250 professionals in 2024 through a structured questionnaire measured on a five-point Likert scale. The data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS 3) to examine the relationships among system integration, data architecture and integration, AI and business intelligence capability, real-time decision-making, and financial operational performance. The results reveal that system integration significantly enhances data integration and AI-enabled analytical capability, which in turn improves real-time decision-making. Real-time decision-making emerges as the strongest predictor of improved financial operational performance, particularly in budgeting and forecasting processes. Furthermore, the findings demonstrate that the impact of system integration on financial performance is largely mediated by data integration, AI and BI capability, and decision-making capability. This study contributes to the digital transformation literature by providing empirical evidence from a multi-cloud context and offers practical insights for Energy and Utilities organizations seeking to leverage AI and data strategies to achieve agile, data-driven financial management.
Strengthening True Performance Accountability: Seamless Integration Between Financial Systems and The Cloud to Gain Real-Time Insights into Budget Costs Kusumba, Surender
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 01 (2024): 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.v2i01.867

Abstract

Strengthening performance accountability has become increasingly important for organizations operating in complex and data-intensive environments, particularly within the energy and utilities sector in the United States. Fragmented financial systems and delayed budget reporting often limit transparency, weaken cost control, and constrain managerial accountability. This study examines how seamless integration between financial systems and cloud-based platforms facilitates genuine performance accountability through real-time budget insights. Adopting a quantitative research design, data were collected from 300 professionals working in finance, accounting, management, and information systems roles within U.S. energy and utilities organizations. The data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS 3). The findings reveal that financial system integration and cloud capability both have significant positive effects on real-time budget insights and true performance accountability. Moreover, real-time budget insights partially mediate the relationships between financial system integration and performance accountability, as well as between cloud capability and performance accountability. These results demonstrate that digital financial infrastructure strengthens accountability most effectively when it generates continuous, real-time budget visibility that supports timely decision-making and transparent financial oversight. This study contributes to the literature on digital finance and performance management by empirically positioning real-time budget insights as a critical mechanism linking cloud-enabled financial integration to accountability outcomes. Practically, the findings offer guidance for organizations seeking to enhance budget transparency and accountability through integrated and cloud-based financial systems.
Integrating Wearable Health Data and Environmental Management Analytics for AI-Driven Cardiovascular Disease Prevention Sabiha Nusrat; Hossain, Forhad; Sikder, Tawfiqur Rahman
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 02 (2024): 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.v2i02.868

Abstract

Cardiovascular disease (CVD) is currently the top global cause of death, and is caused by complex interactions between physiological, behavioral, and environmental factors. Although wearable health technologies used in conjunction with artificial intelligence (AI) have made it possible to monitor cardiovascular functions continuously, most current systems only monitor physiological signals, while neglecting environmental factors that play important roles in cardiovascular risk. This study is a proposal for the integrated process of an Artificial Intelligence-driven framework to combine with wearable health data and environmental management analytics for real-time cardiovascular disease prevention measures. Building on established deep learning methodologies for wearable-based monitoring - in this case, Long Short Term Memory (LSTM) and Convolution neural network (CNN) models - the approach also includes environmental variables as air quality indices, ambient temperature, humidity, and urban stress indicators (Miah, M. A., et al., 2019). Multimodal time series data are preprocessed, synchronized, and analyzed by a hybrid convergent CNN & one-dimensional long short-term memory network to obtain personalized cardiovascular risk prediction. Experimental results have shown that combining environmental analytics predicts more accurately and with fewer false alarms (Dominique, Dyvand & Mote, 2019), particularly in poor environmental conditions. The proposed framework proposes to further develop preventive cardiology by facilitating context-aware, personalized, and scalable cardiovascular risk management that offers significant implications in precision public health, smart city, and sustainable healthcare systems.
Cybersecurity in ERP-Integrated Supply Chains: Risks and Mitigation Strategies Khokrale, Ravindra
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.v3i02.869

Abstract

Cybersecurity risks have emerged as a burning issue as global supply chains increasingly use Enterprise Resource Planning (ERP) systems to integrate official systems into their supply chains. ERP systems unite different stakeholders, including suppliers, logistics, and finance teams, making it possible to exchange real-time information and streamline it. However, there is a possibility of cyberattacks in these systems, particularly when integrating with third-party systems, having poor access control, and using outdated software. The emergence of high-profile attacks such as the 2017 NotPetya has underscored the dramatic financial and operational loss factors because of ERP breaches and outlined the importance of firm protection against cyberattacks. This paper discusses the most significant cybersecurity threats to ERP-integrated supply chains and voices the successful mitigation measures. Major risks observed are the vulnerability of third parties, weak access control, and the use of old ERP systems. Such measures as multi-factor authentication, continuous monitoring, and vendor risk management are also evaluated as the best practices of the study. The study provides effective suggestions that can be implemented in organizations to ensure that their ERP-based supply chains are secured, and the chances of data breaches and disruptions in operations are reduced. With the digitalization of supply chains, the future is seen to utilize the new capabilities to use new technologies, including artificial intelligence and blockchain, to further improve the security and information integrity of ERP.

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