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International Journal of Engineering, Science and Information Technology
Published by Universitas Malikussaleh
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Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 673 Documents
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Deep Learning-Based Worker Posture Classification for Ergonomic Risk Evaluation in Manufacturing Rahmadwati Rahmadwati; Farrel Rafif Ferdian; Yeni Sumantri; Dhaffin Rayhzan Ferdian
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.v6i3.1878

Abstract

Continuous ergonomic monitoring in manufacturing remains challenging because conventional posture assessment methods rely on manual observation, making evaluations time-consuming, subjective, and unsuitable for continuous industrial applications. This study proposes an automated ergonomic risk assessment framework that integrates Media Pipe Pose with a Convolutional Neural Network (CNN) to classify worker postures into low-, medium-, and high-risk ergonomic categories. The framework extracts 33 anatomical body landmarks from RGB images and video frames to generate marker less posture representations for deep learning-based classification. A dataset consisting of 4,500 posture samples collected from assembly, packaging, and welding workstations was expanded to 12,000 samples through data augmentation techniques, including rotation, scaling, horizontal flipping, and brightness adjustment, to improve model robustness and generalization. The CNN model was trained and evaluated using an independent test dataset, achieving an overall classification accuracy of 94.2%, with precision, recall, and F1-score consistently exceeding 94% across all ergonomic risk categories. Comparative evaluation against a conventional REBA/RULA-based rule-driven assessment demonstrated that the proposed framework improved classification accuracy by 7.5 percentage points while eliminating the need for manual posture scoring and reducing observer subjectivity. Furthermore, computational performance analysis showed that the complete inference pipeline operated at an average of 14 ms per frame (approximately 28.5 FPS) on a standard Intel Core i7 CPU with 16 GB RAM, without requiring GPU acceleration, indicating its suitability for real-time deployment in manufacturing environments. The proposed Media Pipe–CNN framework provides an efficient, accurate, and marker less solution for automated ergonomic risk assessment, supporting intelligent occupational safety management, continuous workplace monitoring, and the implementation of smart manufacturing systems aligned with Industry 4.0 initiatives
Women in Construction Sector: An Integrative Literature Review and SWOT Analysis for Strategic Development Irika Widiasanti; Iris Mahani; Yunita Afliana Messah; Selvia Agustina
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.v6i1.1508

Abstract

Women's participation in the construction sector remains significantly low on a global scale. This persistent underrepresentation is primarily driven by multiple systemic barriers that severely limit career advancement, retention, and professional development for female practitioners. Consequently, there is a critical need for comprehensive systematic analysis and strategic solutions to address these challenges within the architecture, engineering, and construction (AEC) industries. This study aims to systematically analyze the multifaceted challenges, emerging opportunities, and practical strategic solutions for enhancing women's involvement in the construction industry through an integrative methodological approach. The research methodology encompasses a comprehensive integrative literature review combined with a robust SWOT analysis framework, involving extensive database searches across major academic repositories, strict quality assessments, and systematic data extraction. The findings identify critical internal barriers such as workplace discrimination, safety concerns, work-life balance challenges, and limited career progression pathways. However, favorable external opportunities such as technological advancements, evolving industry policy reforms, and severe labor shortages creating high demand provide viable avenues for long-term professional growth and integration. Furthermore, this research proposes structured strategic development pathways, including targeted training programs, flexible work arrangements, institutionalized mentoring, and the cultivation of an inclusive organizational culture. By bridging the gap between internal diagnostics and external environmental scanning, this paper contributes an evidence-based strategic framework and actionable recommendations for diverse industry stakeholders. Ultimately, it fosters a sustainable development pathway for gender equality, ensuring that women's participation is recognized not merely as a compliance measure, but as a strategic catalyst for long-term industry innovation, workforce resilience, and overall organizational efficiency in the built environment
Automating Institutional Knowledge: A Dynamic, AI-Driven Knowledge Graph Architecture for Safety-Critical Codebases Parth Govind Vanparia
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.v6i3.1871

Abstract

Safety-critical software systems in the automotive, aerospace, and industrial sectors have grown to millions of lines of source code, making traditional approaches to software documentation, architecture comprehension, and knowledge transfer increasingly impractical. As continuous integration and rapid release cycles accelerate software evolution, organizations face persistent challenges related to technical drift, loss of domain expertise, fragmented architectural knowledge, and the dependence on a small number of senior developers. Conventional code discovery techniques based on lexical or keyword searches provide limited support because they identify only literal text rather than the underlying functional semantics of software artifacts. This paper proposes the Dynamic Code Wiki, an intelligent software knowledge architecture that automatically transforms large codebases into continuously evolving, semantically searchable knowledge repositories. The proposed framework integrates Abstract Syntax Tree (AST) parsing, vector-based semantic embeddings, retrieval-augmented generation (RAG), and automated knowledge graph construction to generate structured documentation directly from source code and development artifacts. The architecture further strengthens compliance with functional safety standards by automatically linking software requirements to implementation components and performing call-graph analysis to identify subsystems affected by code modifications. In addition, engineering knowledge embedded within commit messages, code review discussions, defect reports, and software evolution history is preserved as a permanent, searchable organizational knowledge graph independent of individual developers. By combining semantic code understanding with automated documentation and traceability, the proposed framework significantly improves software maintainability, architectural transparency, impact analysis, and long-term knowledge preservation. The Dynamic Code Wiki provides a scalable engineering solution for modern safety-critical software development, enabling organizations to reduce knowledge loss, accelerate developer onboarding, strengthen regulatory compliance, and support continuous software evolution without relying solely on manually maintained documentation or the institutional memory of experienced engineers
Provisioning AI-Ready Infrastructure at Scale: Engineering Considerations for Infrastructure Architects Hemanth Kumar Gandavarapu
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.v6i3.1900

Abstract

Enterprise adoption of artificial intelligence is restructuring the discipline of infrastructure planning in ways that conventional capacity models cannot accommodate. Artificial intelligence workloads span a heterogeneous spectrum of training, fine-tuning, inference, and batch scoring operations, each imposing qualitatively distinct demands on accelerator compute, storage throughput, and network fabric. The proliferation of graphics processing unit-accelerated clusters, high-bandwidth interconnects, and multi-cloud execution environments has rendered traditional provisioning frameworks inadequate for governing the scale, velocity, and compliance complexity inherent to production artificial intelligence platforms. This article presents a practitioner-oriented engineering framework for provisioning artificial intelligence-ready infrastructure that remains architecturally stable across accelerator generations, managed service evolutions, and organizational growth trajectories. Drawing on operational patterns from large-scale cloud transformation programs, the framework addresses workload segmentation, layered platform architecture, accelerator cluster governance, data provenance, network engineering, security, reliability, and cost governance as interdependent engineering concerns. The central argument is that organizations achieving sustained operational excellence in artificial intelligence infrastructure do so through deliberate platform architecture governed by automation-first operational practices, not through hardware procurement alone. The article concludes by projecting the long-term strategic implications of multi-cloud artificial intelligence transformation as a governed maturity progression, offering forward-looking guidance for infrastructure architects navigating an accelerating and mission-critical technology landscape
Performance Analysis of Ensemble Learning Models Comparing Bagging and Boosting Techniques for Early Preeclampsia Risk Detection in Pregnant Women Prediction Yudhi Saputra; Milkhatun Milkhatun; Aldi Bastiatul Fawait; Zakaria Ahmad Dahlan; Yazeed Al Moaiad; Haviluddin Haviluddin; Rayner Alfred
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.v6i1.1880

Abstract

Preeclampsia is a major pregnancy complication that substantially contributes to maternal morbidity and mortality worldwide, making early identification of risk factors essential for effective prevention and timely clinical intervention. This study evaluates the performance of ensemble learning models by comparing bagging and boosting techniques to develop an accurate early prediction system for preeclampsia risk using clinical medical record data. The research follows the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, encompassing data understanding, data preparation, modeling, evaluation, and interpretation. The dataset was obtained from RSUD Inche Abdoel Moeis Samarinda and underwent preprocessing procedures, including data cleaning, transformation, feature encoding, normalization, and Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. Six ensemble learning algorithms were evaluated, consisting of Random Forest, Extra Trees, and Rotation Forest as bagging methods, and XGBoost, LightGBM, and CatBoost as boosting methods. Model performance was assessed using accuracy, precision, recall, and weighted F1-score. The experimental results demonstrate that Random Forest achieved the highest predictive performance, with an accuracy of 0.92, precision of 0.93, recall of 0.92, and weighted F1-score of 0.91, indicating superior robustness and generalization capability. Extra Trees achieved comparable accuracy (0.92) but exhibited lower prediction stability across evaluation metrics. Among the boosting algorithms, LightGBM and CatBoost each obtained an accuracy of 0.89, while XGBoost achieved 0.88. Rotation Forest recorded the lowest accuracy (0.62), suggesting limited suitability for this clinical dataset. These findings indicate that bagging-based ensemble methods, particularly Random Forest, outperform boosting techniques for imbalanced clinical data and provide strong empirical support for developing reliable Clinical Decision Support Systems (CDSS) for early preeclampsia screening and risk assessment in healthcare settings
Beyond Proof of Concept: A Structured Economic Framework for Governing AI Value Realization in Enterprise Cloud Architectures Kiran Kumar Jaghni
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.v6i3.1867

Abstract

Despite sustained growth in enterprise artificial intelligence (AI) investment, relatively few organizations achieve measurable business value from production-scale deployments. Industry evidence indicates that most enterprises have yet to demonstrate significant financial returns from AI initiatives, while a substantial proportion of projects fail to progress beyond pilot or proof-of-concept stages. This persistent value gap is primarily architectural rather than algorithmic, reflecting the absence of governance-first decision frameworks capable of evaluating AI suitability before investment, accounting for the full lifecycle cost of AI systems, and continuously monitoring realized business value after deployment. This paper introduces the AI Value Realization Framework (AVRF), a five-gate governance model designed to support evidence-based AI investment decisions within cloud-native enterprise environments. The framework integrates three complementary analytical instruments. The AI Suitability Assessment Model (AISAM) evaluates eleven pre-investment criteria using a calibrated 0–35 scoring mechanism to determine whether a proposed business problem genuinely requires AI-based solutions. The AI Value Efficiency Ratio (AVER) measures the relationship between realized business value and a comprehensive five-layer AI cost structure, including infrastructure, data, model operations, governance, and organizational adoption costs. Finally, a continuous value realization cycle aligns governance activities with enterprise AI maturity to support ongoing performance evaluation and strategic optimization. The applicability of AVRF is examined in healthcare platform modernization and government digital transformation, where regulatory compliance, multi-tenant data isolation, transparency, and accountability significantly increase governance complexity. The study argues that organizations deploying AI without structured governance and value measurement mechanisms are likely to produce AVER values below 1.0, indicating that implementation costs exceed measurable business benefits regardless of model sophistication. The proposed framework provides a practical governance architecture for improving AI investment decisions, operational accountability, and sustainable enterprise value realization
Intelligent API Automation Framework for Enterprise Microservices: A Four-Layer Architecture for Governance, Orchestration, and Telemetry-Driven Optimization Jyothirmai Gurramula
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.v6i3.1890

Abstract

Enterprise microservices ecosystems have become central to modern financial and technology platforms, yet they introduce persistent governance challenges, including fragmented API contracts, undifferentiated traffic policies, siloed telemetry, and brittle workflow orchestration that can fail under load. Existing integration middleware typically addresses these concerns independently rather than through a coherent architectural model. This paper proposes the Intelligent API Automation Framework (IAAF), a four-layer reference architecture designed to systematically address these limitations. Layer 1, API Design and Governance, enforces schema-first contract discipline, automated lint validation, and lifecycle management across hundreds of microservice endpoints. Layer 2, Workflow Orchestration, coordinates multi-service transactions through declarative saga choreography, reducing dependency on distributed locking mechanisms. Layer 3, Decision Automation, integrates policy engines and machine learning-assisted routing logic within the gateway plane, enabling context-aware traffic shaping without requiring application-layer modification. Layer 4, Telemetry Optimization, combines structured logging, distributed tracing, and service-level-objective (SLO)-aligned alerting within a unified observability pipeline. Empirical validation using a financial services reference implementation demonstrates that IAAF reduces API governance violations by 73%, improves workflow completion rates by 68%, and decreases mean time to detect (MTTD) by 61% compared with baseline middleware configurations. These findings indicate that IAAF provides an integrated and practical architectural approach for operationalizing intelligent API automation at enterprise scale. The framework enables organizations to strengthen governance, improve transaction reliability, optimize traffic management, and enhance operational visibility while maintaining flexibility across complex microservices environments and supporting scalable digital transformation initiatives. The results further highlight its potential for resilient, scalable, and adaptive enterprise operations
Human-AI Collaboration Models for Scalable Enterprise Software Testing Rejenish Kiran
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.v6i3.1872

Abstract

Enterprise software testing organizations must balance the scalability required by continuous delivery with the contextual judgment necessary for effective quality assurance. Although automated testing enables rapid execution and extensive regression coverage, it lacks the domain expertise, business context, risk awareness, and ethical accountability required for critical release decisions. This paper proposes two complementary governance frameworks that establish a structured model for human–AI collaboration in enterprise software testing. The Human–AI Responsibility Allocation (HARA) Model defines the optimal distribution of testing activities based on comparative strengths, assigning repetitive and computationally intensive tasks—including regression testing, pattern recognition, anomaly detection, and test execution—to artificial intelligence, while reserving strategic responsibilities such as test planning, defect prioritization, release readiness assessment, governance, and compliance oversight for human experts. To operationalize this allocation, the AI Confidence-Based Escalation Framework (ACEF) introduces a three-tier escalation mechanism that dynamically determines when AI-generated testing outcomes require human review according to confidence scores, business criticality, and organizational risk tolerance. The framework further incorporates measurable governance indicators, including escalation rate, false-positive rate, human override frequency, model drift, and decision traceability, enabling continuous monitoring of AI performance and accountability. The proposed frameworks are evaluated conceptually across regulated enterprise environments, including insurance, financial services, and healthcare, where software quality directly affects regulatory compliance, operational resilience, and customer trust. The analysis demonstrates that clearly defined accountability boundaries enable organizations to achieve the speed and scalability of AI-assisted testing while preserving human judgment for high-risk decisions. The proposed governance architecture provides a practical foundation for responsible AI adoption in software quality assurance by improving testing efficiency, auditability, transparency, regulatory compliance, and organizational confidence in AI-supported continuous delivery practices without compromising human oversight or decision accountability
Integrated Ergonomic Risk Assessment Using NBM, REBA and NIOSH Multitask Analysis for Sustainable Logistics: A Case Study of PT SHJ Theresia Amelia Pawitra; Siagian Jevendra Vablo Gloria; Yudi Sukmono
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.v6i1.1881

Abstract

Musculoskeletal disorders (MSDs) remain one of the leading occupational health problems in manual material handling activities, adversely affecting workers' well-being, operational efficiency, and long-term organizational sustainability. This study aimed to comprehensively assess ergonomic risks in the Logistics Department of PT Surya Hutani Jaya, an industrial plantation forestry company in Indonesia, by integrating workers' subjective complaints with objective ergonomic and biomechanical assessments. A descriptive quantitative case study was conducted using a total sampling approach involving five logistics workers performing three critical activities: incoming goods handling, chemical mixing, and fuel inspection. Data were collected using the Nordic Body Map (NBM) questionnaire to identify musculoskeletal complaints, the Rapid Entire Body Assessment (REBA) method to evaluate postural risks, and the Revised NIOSH Lifting Equation with a multitask analysis approach to determine cumulative lifting risks through the Composite Lifting Index (CLI). The findings revealed that incoming goods handling presented the highest ergonomic risk, with an NBM score of 51, a REBA score of 9 (high risk), and a CLI value of 1.065, exceeding the recommended safety threshold. Chemical mixing also demonstrated considerable postural risk with a maximum REBA score of 7, while fuel inspection showed a moderate ergonomic risk with a REBA score of 6, primarily caused by prolonged trunk flexion during manual measurement. The consistency between subjective complaints and objective assessment results confirmed the effectiveness of the integrated evaluation approach in identifying priority areas for intervention. Based on the hierarchy of risk controls, engineering interventions—including the implementation of a scissor lift pallet truck, redesign of the chemical mixing workstation with an ergonomic platform, and replacement of conventional fuel measuring tools with longer or digital measuring devices—were proposed to minimize musculoskeletal risks. The integration of NBM, REBA, and NIOSH multitask analysis provides a comprehensive ergonomic assessment framework that supports safer work systems, improves operational performance, and contributes to sustainable logistics operations by enhancing worker health, productivity, and occupational safety
Resilient Supply Chain Risk Prediction Using Graph-Based Learning for Regional Food Distribution Networks Nurlaela Kumala Dewi; Cut Ita Erliana; Khana Wijaya; Hayati Hehamahua
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.v6i3.1887

Abstract

Supply chain disruptions in regional food distribution networks can generate cascading impacts due to complex interdependencies among producers, warehouses, distributors, retailers, and other supply chain entities. Existing risk prediction approaches often rely on static representations and independent feature analysis, limiting their ability to capture dynamic network evolution, risk propagation mechanisms, and interpretable decision support. This study proposes a Resilient Supply Chain Risk Prediction framework using graph-based learning for a regional rice distribution network in Aceh Besar Regency and Banda Aceh City, Indonesia. The proposed framework integrates Dynamic Supply Chain Graphs, Temporal Graph Neural Networks (Temporal GNNs), risk propagation analysis, and explainable artificial intelligence (XAI) to model evolving supply chain relationships and identify disruption vulnerabilities. Operational data from rice mills, warehouses, distributors, and traditional markets are transformed into sequential dynamic graph representations to capture changes in supply availability, inventory conditions, transportation relationships, demand variations, and disruption patterns over time. Experimental results demonstrate that the proposed Temporal GNN outperforms Random Forest, XGBoost, and LSTM models, achieving 92.40% accuracy, 92.49% F1-score, and an AUC-ROC of 0.968. Furthermore, risk propagation analysis identifies critical nodes and disruption pathways within the supply chain network, while explainability analysis using GNNExplainer and SHAP reveals that lead time, inventory level, and supplier reliability are key factors influencing disruption risks. The proposed framework also supports resilience-oriented decision-making by enabling proactive mitigation strategies, including route adjustment, inventory redistribution, and prioritization of critical supply chain entities. This study contributes to supply chain risk analytics by providing an interpretable dynamic graph-based learning framework that simultaneously captures network evolution, temporal risk patterns, cascading disruption behavior, and resilience improvement strategies for regional food distribution systems

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