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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 145 Documents
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Integration of DevOps Practices for Continuous Delivery and System Optimization Afrin Zaman; Khalilur Rahman; Amit Dhali; Jhon Kabir; Antu Roy
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.v2i02.1076

Abstract

The growing demand for rapid software delivery and efficient system performance has led to the widespread adoption of DevOps practices in modern software engineering. This study evaluates the integration of DevOps practices for achieving continuous delivery and system optimization by comparing traditional development models, Agile methodologies, DevOps, and DevOps combined with continuous delivery (CD). The analysis is supported by three figures illustrating key performance metrics, including deployment frequency, lead time, change failure rate, mean time to recovery (MTTR), response time, throughput, resource utilization, and system availability. The findings indicate that traditional development approaches exhibit low deployment frequency, high lead time, and poor system performance due to limited automation and rigid processes. Agile methodologies improve flexibility and reduce development cycles; however, they provide only moderate improvements in operational performance. In contrast, DevOps practices significantly enhance system efficiency by integrating development and operations, enabling continuous integration, automated testing, and faster deployment cycles. The results highlight the importance of automation, collaboration, and continuous monitoring in achieving system optimization. By enabling rapid and reliable software releases, DevOps practices contribute to improved system reliability and scalability in dynamic environments. This study provides valuable insights for organizations seeking to optimize their software development processes and adopt modern DevOps practices to achieve continuous delivery and high system performance.
Microservices-Based System Design for Ensuring High Availability and System Reliability Khalid Khan; Zulfiqur Rahman; Amrita Khan; Anamika Roy; Jhon Kabir
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.v2i02.1077

Abstract

The increasing demand for highly available and reliable software systems has driven the adoption of microservices-based architectures in modern distributed environments. The analysis is supported by two figures that illustrate key performance metrics such as system availability, mean time to repair (MTTR), mean time to failure (MTTF), response time, throughput, and failure rate under dynamic workloads. The results indicate that monolithic systems exhibit lower availability, higher response time, and increased failure rates, making them less suitable for modern, dynamic environments. The transition to microservices architecture improves fault isolation and scalability, resulting in enhanced reliability and reduced downtime. Further improvements are observed with the integration of load balancing mechanisms, which distribute workload efficiently across service instances, thereby increasing system resilience. The highest performance is achieved when microservices are combined with orchestration platforms such as Kubernetes. This configuration demonstrates near-optimal availability, minimal recovery time, and superior scalability, as evidenced by reduced response times and increased throughput. The study highlights the critical role of architectural design and supporting technologies in achieving high availability and reliability. The findings provide valuable insights for system designers and organizations aiming to build robust and scalable applications. Overall, microservices-based system design, when combined with advanced deployment and management strategies, offers a powerful solution for addressing the challenges of modern distributed systems.
Operational Challenges in Basel IV Credit Risk Compliance Ravikumar Mani Naidu Gunasekaran
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.v1i01.1089

Abstract

The implementation of Basel IV regulations represents a significant advancement in global banking supervision, with a strong focus on enhancing the accuracy, consistency, and transparency of credit risk measurement. While these reforms strengthen the resilience of financial institutions, they introduce substantial operational complexities, particularly in the areas of data management, system integration, model governance, and regulatory reporting. This paper examines the key operational challenges faced by banks in complying with Basel IV credit risk requirements, including the adoption of revised standardized approaches, restrictions on internal ratings-based (IRB) models, and the introduction of the output floor. The study highlights critical issues such as fragmented data architectures, legacy system constraints, increased computational demands, and the need for robust data lineage and governance frameworks. Additionally, the paper discusses the implications of heightened regulatory scrutiny and the requirement for greater model transparency and validation under evolving compliance standards. To address these challenges, the paper outlines strategic approaches involving modernization of technology infrastructure, adoption of cloud-based platforms, automation of reporting processes, and integration of advanced analytics. By providing a comprehensive assessment of operational barriers and potential solutions, this study aims to support financial institutions in navigating the complexities of Basel IV implementation. The findings underscore the importance of aligning organizational processes, technology, and governance frameworks to achieve effective and sustainable compliance in an increasingly data-driven regulatory environment.
From Observability to Closed-Loop AIOps: Data-Driven Automation for Secure and Resilient Network Operations Mohit Bajpai
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.v1i02.1090

Abstract

Modern enterprise and service-provider networks are now distributed across cloud, edge, software-defined data centers, mobile access, Internet of Things (IoT), and hybrid work environments. The operational challenge is no longer limited to device availability; teams must interpret high-volume telemetry, fast-changing application paths, user-experience signals, identity context, security events, and configuration drift at machine speed. This updated article expands the original discussion of AI Ops, machine learning, observability, and network security by adding a data-centered reference architecture, operational metrics, model-selection considerations, security controls, deployment phases, and governance requirements. The article explains how telemetry from SNMP, streaming telemetry, NetFlow/IPFIX, syslog, OpenTelemetry, endpoint logs, cloud logs, configuration repositories, and security tools can be converted into actionable intelligence through anomaly detection, forecasting, causal correlation, risk scoring, and policy-based automation. It also positions closed-loop AIOps as a practical operating model that improves mean time to detect, mean time to acknowledge, mean time to resolve, service-level compliance, capacity planning, and security response while preserving human approval for high-risk actions.
Liquidity Risk Modeling with Machine Learning: Big Data Approaches for Intraday Liquidity Prediction Ravikumar Mani Naidu Gunasekaran
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.v1i02.1096

Abstract

Liquidity risk has emerged as a critical concern for financial institutions due to increasing market volatility, regulatory scrutiny, and the growing complexity of global financial systems. Traditional liquidity risk management approaches, which rely on static assumptions and low-frequency data, are often inadequate for capturing rapid intraday fluctuations in cash flows and funding requirements. This paper explores the application of machine learning techniques combined with big data architectures to enhance intraday liquidity prediction and risk modeling. The study presents a data-driven framework that leverages high-frequency transactional data, market indicators, and behavioral patterns to forecast liquidity positions in near real time. Advanced machine learning models, including ensemble methods and deep learning architecture such as Long Short-Term Memory (LSTM) networks are evaluated for their ability to capture nonlinear dependencies and temporal dynamics inherent in liquidity flows. The proposed approach integrates scalable big data technologies to support real-time ingestion, processing, and predictive analytics. Results demonstrate that machine learning-based models significantly outperform traditional methods in forecasting accuracy and responsiveness to market stress conditions. The paper also discusses practical implementation considerations, including model interpretability, regulatory compliance, and integration with enterprise treasury systems. By enabling proactive liquidity management and early detection of stress scenarios, the proposed framework offers substantial improvements in financial resilience and operational efficiency for modern banking institutions.
Generative AI: Opportunities, risks and implications for Financial Services Ravikumar Mani Naidu Gunasekaran
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 03 (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.v1i03.1098

Abstract

Generative Artificial Intelligence (GenAI) is rapidly transforming the financial services industry by enabling advanced automation, intelligent decision-making, and enhanced customer experiences. Technologies such as large language models and generative models are reshaping processes across risk management, fraud detection, regulatory reporting, and customer engagement. However, the adoption of GenAI introduces significant challenges, including model risks, data privacy concerns, regulatory uncertainties, and ethical implications. This paper explores the opportunities and risks associated with generative AI in financial services and proposes a structured framework for responsible adoption. By integrating governance, risk management, and regulatory compliance mechanisms, the study provides practical insights for financial institutions seeking to leverage GenAI while ensuring security, transparency, and resilience.
From Data to Decisions: How Quality Drives Machine Learning Success Ravikumar Mani Naidu Gunasekaran
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.v2i01.1099

Abstract

In the era of data-driven decision-making, machine learning (ML) has emerged as a critical tool for extracting insights and enabling intelligent automation across industries. However, the success of ML models is fundamentally dependent on the quality of the data used throughout the analytics pipeline. This article explores the relationship between data quality and machine learning performance, emphasizing how data integrity directly impacts model accuracy, reliability, and fairness. Key dimensions of data quality—including accuracy, completeness, consistency, and timeliness—are examined in the context of real-world ML applications. The article further discusses common data challenges such as missing values, noise, bias, and data drift, highlighting their implications on predictive outcomes. Additionally, it presents practical approaches to improving data quality through data preprocessing, validation, governance frameworks, and automated monitoring systems. By bridging the gap between raw data and actionable insights, this study underscores that high-quality data is not merely a prerequisite but a strategic enabler of successful machine learning initiatives. Organizations that prioritize data integrity can achieve more robust models, better decision-making, and sustain competitive advantage in an increasingly data-centric world.
Autonomous Network Troubleshooting with AIOps and Machine Learning: A Data-Driven Architecture for Correlation, Prediction, and Remediation Mohit Bajpai
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.v2i02.1101

Abstract

Modern networks carry business-critical traffic across data centers, cloud platforms, branch locations, APIs, telecom circuits, security gateways, and software-defined overlays. In this environment, traditional troubleshooting is no longer limited by the availability of alarms; it is limited by the volume, fragmentation, and operational interpretation of telemetry. This updated paper presents an expanded AIOps and machine learning framework for automating network troubleshooting through telemetry ingestion, data enrichment, anomaly detection, event correlation, root-cause ranking, predictive risk scoring, and governed closed-loop remediation. The paper extends the original architecture by adding data governance, model lifecycle management, explainability, human approval gates, operational KPIs, and security controls. It also introduces a telecommunications implementation scenario in which Remedy tickets, Kafka streams, Kong APIs, topology data, and AIOps model outputs are combined to reduce alert noise, accelerate diagnosis, and improve network resilience. The proposed approach is not intended to replace network engineers; rather, it converts repetitive investigation patterns into repeatable, auditable, and continuously improving operational workflows.
Agentic AI-Enhanced Network Performance Monitoring and Diagnostic Analysis for Site Reliability Engineering Mohit Bajpai
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.v3i01.1102

Abstract

Network performance monitoring and diagnostic analysis (NPMD) is becoming a core reliability discipline for modern distributed systems because cloud applications, hybrid connectivity, software-defined networking, and multi-region dependency chains can turn small network degradations into visible service incidents. The original paper explained NPMD through Site Reliability Engineering (SRE) concepts such as service level indicators (SLIs), service level objectives (SLOs), and non-functional requirements. This updated version expands the work with a data-driven operating model, stronger references, explicit table and figure captions, and an Agentic AI solution pattern for bounded autonomous diagnosis and remediation. The proposed approach combines telemetry pipelines, SLO evaluation, topology and change evidence, retrieval-augmented diagnostic reasoning, runbook-constrained tool execution, and human approval controls. The paper treats AI as an operational assistant rather than an uncontrolled replacement for SRE judgment: the agent can summarize evidence, correlate probable causes, recommend remediation, and execute only low-risk approved actions while preserving auditability. The result is a practical framework for reducing alert noise, improving time to detect, accelerating incident triage, and strengthening post-incident learning without relying on unsupported claims or unverifiable performance numbers.
Impact of Custom Interface Widgets on User Efficiency in Content Navigation Platforms Ravi Kiran Kanneganti
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.v1i01.1121

Abstract

The simplest unit of functionality of user interface with digital systems is interface widgets. In spite of their seeming simplicity, the type, location, and design reasoning to the background of such widgets is what defines the efficiency with which a user navigates material-dense sites. This research looks into adjusting the design properties of a widget to the features of user efficiency (time taken to complete a task, rate of errors and cognitive load). This study involves 120 subjects in an experimental design with mixed methods and on three content navigator platforms and it measures performance based on 5 forms of widgets which include menus, scrollable conglomerates, adaptive items, navigation aids and controller of forms. Findings reveal the reduction in task completion times is an average of 18.4 more in hierarchical structure than mixed-structure alternatives and that in adaptive promotion of widgets, the error rates were less by 21.3. There is also a reduction in the cognitive load as captured through the fixations of the eye which is significantly reduced in linear navigation settings. These represent some of the quantitative constructs that can support the current qualitative recommendations and add design specifications to web, mobile and embedded content platforms.

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