cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Dahlan Abdullah
Contact Email
dahlan@unimal.ac.id
Phone
+62811672332
Journal Mail Official
ijestyjournal@gmail.com
Editorial Address
Jl. Tgk. Chik Ditiro, Lancang Garam, Lhokseumawe, Aceh - Indonesia, 24351
Location
Kota lhokseumawe,
Aceh
INDONESIA
International Journal of Engineering, Science and Information Technology
Published by Universitas Malikussaleh
ISSN : -     EISSN : 27752674     DOI : -
Core Subject : Science, Education, Social, Engineering,
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation
The journal covers all aspects of applied engineering, applied Science and information technology, that is: Engineering: Energy Mechanical Engineering Computing and Artificial Intelligence Applied Biosciences and Bioengineering Environmental and Sustainable Science and Technology Quantum Science and Technology Applied Physics Earth Sciences and Geography Civil Engineering Electrical, Electronics and Communications Engineering Robotics and Automation Marine Engineering Aerospace Science and Engineering Architecture Chemical & Process Structural, Geological & Mining Engineering Industrial Mechanical & Materials Science: Bioscience & Biotechnology Chemistry Food Technology Applied Biosciences and Bioengineering Environmental Health Science Mathematics Statistics Applied Physics Biology Pharmaceutical Science Information Technology: Artificial Intelligence Computer Science Computer Network Data Mining Web Language Programming E-Learning & Multimedia Information System Internet & Mobile Computing Database Data Warehouse Big Data Machine Learning Operating System Algorithm Computer Architecture Computer Security Embedded system Coud Computing Internet of Thing Robotics Computer Hardware Information System Geographical Information System Virtual Reality, Augmented Reality Multimedia Computer Vision Computer Graphics Pattern & Speech Recognition Image processing ICT interaction with society, ICT application in social science, ICT as a social research tool, ICT in education
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 659 Documents
 62   63   64   65   66 
Scaling F5 BIG-IP Load Balancer Automation: Enterprise-Grade Network Transformation Using Ansible and API-Driven Orchestration Uday Kumar Soma
International Journal of Engineering, Science and Information Technology Vol 6, No 3 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i3.1860

Abstract

Enterprise F5 BIG-IP load balancer estates, commonly spanning hundreds to thousands of Traffic Management Operating System devices across geographically distributed data centers, represent a class of infrastructure management challenge where the gap between manual operational capability and automation-required operational scale produces measurable security exposure, configuration drift risk, and delivery velocity constraint. This article presents the engineering principles, implementation architecture, and empirical outcomes of enterprise-scale F5 BIG-IP automation using Ansible and iControl REST API-driven orchestration, derived from the author's primary research in automating a 2,500-device production estate across aviation and financial services operational environments. The article addresses four critical automation engineering challenges: accurate dynamic inventory construction from multiple authoritative sources, pre-deployment dependency validation, preventing silent configuration failures, post-execution state verification, distinguishing reported success from actual convergence, and synchronization-aware orchestration for distributed Global Traffic Manager deployments through four production failure case studies whose analysis yielded architectural improvements now forming the operational standards. Performance outcomes from the author's enterprise deployments demonstrate estate-wide CVE remediation in 18 hours for a 2,500-device estate (versus a six-to-eight-week manual baseline), configuration change success rates of 98.4% with a 1.2% rollback rate across 847 production change executions, and zero-downtime software upgrades across High Availability device pairs using boot location management. The article further examines GitOps-based CI/CD integration, enabling application-speed F5 configuration changes through pull-request governance, artificial intelligence-driven predictive maintenance for anomaly detection before the incident threshold is crossed, and intent-based networking as a trajectory toward natural-language load-balancer policy management. The framework presented provides the engineering foundation for architecturally enabling autonomous network operations for routine load-balancer management tasks
Responsible AI Architecture in Enterprise Modernization: Governance Frameworks, Equity Implications, and Regulatory Convergence Maitray Modi
International Journal of Engineering, Science and Information Technology Vol 6, No 3 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i3.1868

Abstract

The rapid integration of artificial intelligence (AI) into enterprise decision-making systems has fundamentally transformed organizational governance across sectors, enabling automated decisions in credit assessment, healthcare resource allocation, workforce management, pricing strategies, and public-sector services. As AI increasingly influences decisions with significant social and economic consequences, the need for robust governance mechanisms has become as important as technological innovation itself. However, governance frameworks, accountability mechanisms, and equity assessment practices have not advanced at the same pace as AI deployment, creating substantial risks related to transparency, fairness, regulatory compliance, and organizational trust. This article examines three interconnected dimensions of responsible AI for enterprise modernization: governance infrastructure for accountable AI deployment, algorithmic equity in high-impact decision environments, and the evolving international regulatory landscape shaping enterprise AI governance. Drawing upon implementation experiences and governance practices across telecommunications, financial services, and healthcare, the study synthesizes evidence from engineering, policy, ethics, and critical social science literature to develop a comprehensive perspective on responsible AI architecture. The analysis demonstrates that effective AI governance requires integrating technical controls with organizational accountability, continuous monitoring, auditability, risk management, and human oversight throughout the AI lifecycle. Furthermore, the study argues that technical governance alone cannot eliminate algorithmic bias or inequitable outcomes unless accompanied by structural policy interventions addressing the underlying institutional and societal conditions embedded within training data and decision processes. The proposed governance perspective positions responsible AI as a foundational engineering discipline that enhances regulatory compliance, organizational resilience, stakeholder trust, and long-term business sustainability while reducing legal, operational, and reputational risks. The findings provide practical guidance for enterprises seeking to modernize AI-enabled decision systems through governance architectures that balance innovation with accountability, ethical responsibility, transparency, and equitable value creation across increasingly complex digital ecosystems
Quality by Design in Bioprocess Development: Integrating Risk, Data, and Regulatory Strategy Sai Surya Raja Amogha Tenneti
International Journal of Engineering, Science and Information Technology Vol 6, No 3 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i3.1873

Abstract

Quality by Design (QbD) has become a fundamental paradigm in modern biopharmaceutical manufacturing by integrating quality assurance throughout the entire product lifecycle rather than relying solely on end-product testing. This systematic approach emphasizes scientific process understanding, risk-based decision making, and continuous process improvement to ensure consistent product quality and regulatory compliance. This paper examines the application of QbD principles in upstream and integrated bioprocess development, with particular emphasis on establishing robust manufacturing processes through structured risk assessment and data-driven optimization. The framework focuses on the identification and evaluation of Critical Quality Attributes (CQAs) and Critical Process Parameters (CPPs) using formal risk management methodologies to determine the factors that most significantly influence product quality. Experimental Design (Design of Experiments, DoE), multivariate statistical analysis, and advanced process analytical technologies are integrated to characterize process behavior, optimize operating conditions, and define reliable design spaces that support process robustness and scalability. In addition, real-time process monitoring and online measurement technologies enable proactive process control, early deviation detection, and continuous verification of manufacturing performance throughout production. The study further discusses how QbD facilitates lifecycle management by supporting adaptive process optimization, knowledge management, and continuous improvement while meeting evolving regulatory expectations established by international agencies. Particular attention is given to the implementation of Real-Time Release Testing (RTRT) and Continuous Process Verification (CPV) as essential components of modern quality systems that reduce testing delays and improve manufacturing efficiency. The findings demonstrate that QbD provides a comprehensive engineering and quality management framework for developing reproducible, scalable, and compliant biopharmaceutical manufacturing processes. By integrating scientific knowledge, statistical methodologies, risk assessment, and continuous monitoring, QbD enhances product consistency, operational reliability, regulatory compliance, and long-term manufacturing sustainability across the entire bioprocess lifecycle
Digitalization of Transportation 5.0 Through Robotic Fishery Frozen Supply for Enhancing Aquatic Food Distribution in Demak City Muhammad Ikhsan Setiawan; Agus Sukoco; Malikus Sumadyo; Elisa Sulistyorini; Adenanthera Lesmana Dewa; Che Zalina Zulkifli; Nafasov Mirzomurod Mukhamadovich; Fazilat Kodirova
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1827

Abstract

The transformation of fishery product distribution systems has become increasingly important in ensuring food security, supply chain efficiency, and economic resilience in coastal regions. As demand for high-quality aquatic food products continues to grow, conventional logistics systems often face challenges related to post-harvest losses, inefficient transportation, limited traceability, and inadequate cold chain management. This research investigates the application of Digitalization 5.0 in transportation logistics through an integrated Robotic Fishery Frozen Supply system to enhance the distribution of aquatic food products in Demak City, Indonesia. The proposed framework combines autonomous robotic loading and unloading technologies, Internet of Things (IoT)-based cold chain monitoring, real-time logistics management, and blockchain-enabled traceability to create a smart and sustainable fishery distribution ecosystem. The study employs a mixed-methods approach, integrating qualitative assessments through stakeholder interviews and focus group discussions with quantitative evaluations of logistics performance, distribution efficiency, cold chain stability, and operational cost-effectiveness. The findings demonstrate that the implementation of the Robotic Fishery Frozen Supply model significantly improves distribution performance by reducing post-harvest losses by 27%, shortening delivery times by 35%, and enhancing transparency and accountability throughout the supply chain. In addition, the system contributes to maintaining product quality, minimizing food waste, and improving market access for local fishery producers. The research further reveals that the Digitalization 5.0 framework supports sustainable development by integrating human-centered innovation, automation, and environmental responsibility. The proposed model aligns with the Sustainable Development Goals (SDGs), particularly Goal 2 (Zero Hunger), Goal 9 (Industry, Innovation and Infrastructure), and Goal 12 (Responsible Consumption and Production). The novelty of this study lies in the convergence of autonomous robotic technologies, Digitalization 5.0 principles, and sustainable fishery logistics, offering a scalable and replicable model for coastal regions in developing countries seeking to modernize aquatic food distribution systems while strengthening economic sustainability and food security
Transforming Real-Time Transactions: Distributed AI, Fraud Detection, and Advanced Analytics in Regulated Financial Environments Dasaradhi Eddula
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1833

Abstract

The convergence of distributed artificial intelligence (AI), streaming analytics, and real-time payment infrastructure is transforming the financial services sector by enabling faster, more intelligent, and scalable transaction processing. However, this transformation also introduces significant technical, regulatory, and sustainability challenges for financial institutions operating in highly regulated environments. With global fraud losses exceeding USD 485 billion in 2023 and increasing regulatory demands for transparency, fairness, and algorithmic explainability, financial systems must simultaneously achieve high accuracy, low latency, operational efficiency, auditability, and environmental sustainability. This article examines the architectural and operational foundations of compliant distributed AI deployment in modern financial ecosystems. Drawing on current academic and industry literature, the study analyzes the integration of microservices architectures, event-driven processing pipelines, container-based inference engines, edge-cloud computing environments, and federated learning frameworks in supporting real-time fraud detection and payment intelligence. Particular attention is given to fraud detection model architectures, streaming data analytics, distributed model orchestration, and the trade-offs between performance, scalability, and energy consumption. The analysis also explores the broader social and governance dimensions of AI deployment, including fairness, accountability, privacy preservation, regulatory compliance, and carbon-aware computing practices. The findings indicate that well-designed distributed AI systems can achieve fraud-scoring latencies below 20 milliseconds while maintaining high predictive accuracy and compliance with regulatory requirements. Furthermore, federated learning and event-driven architectures enable institutions to improve fraud detection capabilities without centralizing sensitive customer data, thereby enhancing privacy and security. The study concludes that the successful integration of distributed AI and real-time payment infrastructure requires a balanced approach that aligns technological innovation with governance, sustainability, and ethical considerations. The proposed framework offers practical guidance for financial institutions seeking to modernize payment systems while ensuring resilience, trustworthiness, and long-term operational sustainability.
From Batch Prediction to Self-Healing ML Systems: A Production MLOps Framework for Autonomous Model Maintenance Kushwanth Chowdary Kandala
International Journal of Engineering, Science and Information Technology Vol 6, No 3 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i3.1869

Abstract

Machine learning (ML) systems deployed in large-scale healthcare Software-as-a-Service (SaaS) environments frequently experience performance degradation after production release due to data drift, evolving data distributions, pipeline latency anomalies, and declining predictive accuracy. These issues often remain undetected until they significantly affect operational performance, resulting in prolonged incident detection, costly engineering interventions, and increased business risk. This study proposes a self-healing machine learning framework that transforms conventional batch-oriented ML pipelines into autonomous, event-driven operational systems capable of continuously monitoring, diagnosing, and recovering from production failures. The proposed architecture integrates four complementary capabilities: statistical data drift detection, automated retraining triggers, canary-based deployment and promotion workflows, and multi-tier observability that connects model behaviour with automated operational responses. Together, these components establish a closed feedback loop that enables continuous adaptation while minimizing manual intervention. The framework is evaluated through a production case study involving a healthcare long-term care SaaS platform supporting large-scale clinical operations. Empirical results demonstrate substantial operational improvements following deployment of the self-healing architecture. Mean time to detect model drift decreased from 18.4 hours to 2.1 hours, while mean time to recovery was reduced from 9.2 hours to 1.8 hours. In addition, the number of monthly incidents requiring manual intervention declined from 34 to 6, indicating significant gains in operational resilience and engineering efficiency. The proposed framework is implemented using widely adopted open-source technologies, including MLflow, Kubeflow Pipelines, Feast, Great Expectations, and Argo Rollouts, allowing seamless integration with existing Kubernetes-based enterprise infrastructures. The findings demonstrate that autonomous, event-driven maintenance substantially improves the reliability, scalability, and maintainability of production ML systems, providing a practical engineering architecture for resilient AI operations in mission-critical healthcare environments
A Unified Observability Framework for Cloud-Native Machine Learning Systems: Architectural Primitives, Lineage Correlation, and Cross-Stage Reliability Objectives Shankar das Boddu
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1824

Abstract

The observability gap in modern cloud-native organizations reflects a fundamental disconnect between data engineering teams, which focus on pipeline throughput and data freshness, and machine learning teams, which monitor inference latency and prediction drift. This separation is largely caused by telemetry systems that lack a unified identity model capable of linking datasets, features, model versions, and production environments across the machine learning lifecycle. To address this challenge, this paper proposes a unified observability framework that integrates telemetry primitives, lineage-aware correlation, and end-to-end reliability objectives connecting data quality and freshness with model performance outcomes. The framework introduces a minimal set of universal telemetry tags, including environment, workload identifier, dataset/feature/model version, and execution run identifier, enabling consistent cross-lifecycle correlation and incident analysis. A comparative evaluation is conducted against existing observability solutions, including MLflow integrated with OpenTelemetry, Monte Carlo data observability, and WhyLogs. The results indicate that the proposed framework offers superior capabilities for cross-stage incident attribution by linking failures occurring across data pipelines, feature engineering processes, model training, and inference services. Concept validation is performed using the Alibaba Cluster Trace Dataset and Evidently AI Drift Detection Dataset, demonstrating the practicality and applicability of the proposed telemetry primitives in real-world scenarios. The study further shows that lineage-aware correlation can reveal operational dependencies and failure propagation patterns that remain undetected by conventional component-level monitoring approaches. In addition, the framework defines a tool-agnostic event format and supports machine learning–based incident classification for common cross-stage failure modes, including training-serving skew and prediction degradation caused by data freshness issues. An incremental adoption strategy is proposed to facilitate implementation, beginning with high-impact production models and expanding according to demonstrated operational value.
A Framework for Automated Transformation of Legacy SOAP-Based Services to RESTful APIs Anil Kumar Chitiprolu
International Journal of Engineering, Science and Information Technology Vol 6, No 3 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i3.1870

Abstract

Many enterprise information systems continue to rely on legacy Service-Oriented Architecture (SOA) implementations using SOAP-based web services to support critical business operations. Although SOAP provides standardized messaging, strong security, and reliable interoperability, its XML-based communication format and rigid service contracts introduce significant complexity, increase communication overhead, and limit compatibility with modern web and mobile applications. In contrast, RESTful APIs have become the dominant integration paradigm because of their lightweight communication model, scalability, stateless architecture, and native support for JSON-based data exchange. However, replacing legacy SOAP services with newly developed REST APIs is often costly, time-consuming, and disruptive to existing enterprise workflows. This paper proposes an automated middleware framework that transparently transforms legacy SOAP web services into RESTful APIs without modifying existing business logic or backend service implementations. The proposed framework automatically analyzes Web Services Description Language (WSDL) specifications, maps SOAP operations to REST endpoints, and performs bidirectional XML–JSON serialization and deserialization to ensure seamless interoperability between legacy and modern systems. Framework performance is evaluated using three quantitative metrics: the Transformation Efficiency Ratio (TER), measuring transformation correctness; the Payload Reduction Ratio (PRR), measuring communication overhead reduction through JSON serialization; and the Latency Overhead Index (LOI), measuring additional processing delay introduced by the transformation layer. Experimental evaluation was conducted using five representative enterprise SOAP services commonly found in legacy information systems. The proposed framework achieved an average TER of 98.7%, PRR of 63.0%, and LOI of 10.1%, demonstrating high transformation accuracy, substantial payload reduction, and minimal latency overhead. The results indicate that automated middleware-based transformation provides an effective migration strategy for modernizing enterprise integration architectures, enabling organizations to expose lightweight, high-quality RESTful APIs while preserving existing SOAP services, minimizing redevelopment effort, and supporting gradual digital transformation initiatives
Scale Measurement Development for Sustainable Fashion Design Curriculum Through Technopreneur Orientation Rahayu Purnama; Melly Prabawati; Vivi Radiona; Rosita Mohd Tajuddin; Shaliza Mohd Shariff; Muhamad Aiman Afiq Mohd Noor
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1527

Abstract

This article describes the development and validation of the sustainable fashion design curriculum through technopreneur orientation. This quantitative 98-item scale measures Indonesian university fashion design students'(80) toward sustainable fashion design curriculum. The validity and reliability of the scale was statistically tested by calculating the Corrected Item-Total Correlation, Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO MSA) via an Exploratory Factor Analysis, descriptive statistics, Cronbach's Alpha, and a con?rmatory factor analysis. The results of the principal component factor analysis indicate that the scale consists of the following four dimensions: entrepreneurial orientation, technopreneur orientation, fashion sustainability and sustainable fashion design curriculum. The reliability test evaluated the ninety-eight (98) items in the study that were divided into five (4) constructs Entrepreneur Orientation (EO), Technopreneur Orientation (TO), Fashion Sustainability (FS), and Sustainable Fashion Design Curriculum (SFDC). The measurement of the scale are con?rmed by the CFA. Internal reliability was found using the total item correlation value corrected for all items 0.3, Cronbach's Alpha values are more significant than 0.6 and KMO MSA value above 0.5. Cronbach's Alpha coefficients for all constructs showed excellent reliability, with Technopreneur Orientation (TO) having the highest at 0.775 and the lowest at 0.832. Technopreneur Orientation (TO) had the most significant score of 0.832, while Sustainable Fashion Design Curriculum (SFDC) had the lowest score of 0.775. The findings revealed that all 98 items in the four (4) constructs had a considerable internal disagreement. As a result of the pilot investigation, the items in this study's questionnaires were well-founded in previous literature and highly trustworthy and appropriate for study inferences. The results confirm the reliability and validity of the faces of the adapted instrument in the pilot study.

Page 66 of 66 | Total Record : 659

 62   63   64   65   66 

Filter by Year

2021 2026


Reset
Filter By Issues
All Issue Vol 6, No 3 (2026) Vol 6, No 2 (2026) Vol 6, No 1 (2026) Vol 5, No 4 (2025) Vol 5, No 3 (2025) Vol 5, No 2 (2025) Vol 5, No 1 (2025) Vol 4, No 4 (2024) Vol 4, No 3 (2024) Vol 4, No 2 (2024) Vol 4, No 1 (2024) Vol 3, No 4 (2023) Vol 3, No 3 (2023) Vol 3, No 2 (2023) Vol 3, No 1 (2023) Vol 2, No 4 (2022) Vol 2, No 3 (2022) Vol 2, No 2 (2022) Vol 2, No 1 (2022) Vol 1, No 4 (2021) Vol 1, No 3 (2021) Vol 1, No 2 (2021) Vol 1, No 1 (2021) More Issue