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Journal of Renewable Engineering
Published by PT Anagata Sembagi Education
ISSN : -     EISSN : 30467624     DOI : https://doi.org/10.62872/zm22xb92
Core Subject : Engineering,
Automotive Engineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering
The journal publishes original articles on current issues and trends occurring internationally in mechanical engineering, electrical engineering, civil engineering, physical engineering, chemical engineering, industrial engineering, informatics engineering, telecommunications engineering, computer engineering.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 57 Documents
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The Hybrid Specialization Model: Navigating Bureaucratic Rigidity through GovTech Co-creation in Indonesia Ajeng Amalia Andi; Dodie Tricahyono; Agus Salim Andi
Journal of Renewable Engineering Vol. 3 No. 3 (2026): JORE - June
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/kd1gfx84

Abstract

This study investigates how GovTech firms navigate bureaucratic rigidity to accelerate digital maturity in Indonesian local government. It examines the evolution of client needs, the operationalization of collaborative governance, and the structural design of a "Hybrid Specialization Model" within the Electronic-Based Government System (SPBE) framework. Utilizing a qualitative single-case study of the partnership between PT 4nesia Digital Solution and Cirebon City, data were triangulated through semi-structured interviews, field observations, and longitudinal SPBE evaluation reports. Results indicate a shift from technical procurement toward institutional legitimacy, driven by compliance with national SPBE indicators. Success depends on facilitative leadership to bridge sectoral silos. The proposed Hybrid Specialization Model boosted the SPBE Index from 3.17 to 4.13, though a sustainability gap persists requiring the codification of digital workflows into local policy. This research provides empirical evidence for value co-creation and offers a strategic blueprint for shifting from transactional relations to institutionalized partnerships.
Analysis of Factors Affecting Labor Productivity in Small and Medium-Sized Industries Kimberly Febrina Kodrat
Journal of Renewable Engineering Vol. 3 No. 3 (2026): JORE - June
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/tvsadd54

Abstract

Labor productivity remains one of the most decisive determinants of competitiveness and survival for small and medium-sized industries (SMIs), yet the factors that shape it are dispersed across a fragmented body of literature spanning manufacturing, construction, and service contexts. This article synthesizes findings from twenty-five peer-reviewed studies published between 2021 and 2026 to identify, categorize, and interpret the principal drivers of labor productivity in SMIs. Using a narrative literature review method, factors are grouped into four thematic clusters: human capital and workforce competence, capital and technology adoption, work environment and managerial practice, and institutional or policy context. The synthesis shows that human capital consistently exerts the strongest and most stable positive influence, followed by technology adoption, whose effect is conditional on firm size and absorptive capacity. Work environment and compensation factors matter most in labor-intensive small industries, while institutional support determines whether productivity gains are sustained over time. The novelty of this article lies in integrating evidence across sectors and regions into a single comparative framework rather than treating each determinant in isolation. The findings offer practical guidance for SMI owners and policymakers seeking to prioritize interventions, and they identify gaps for future empirical research, particularly on the interaction effects between human capital and digital technology in resource-constrained small industries.
A Three-Layer Cyber AI Governance Framework for Accountable Reinforcement Learning in National Data Sovereignty Andi Agus Salim; Hasbu Naim Syaddad; Luki Ishwara; Agus Nursikuwagus; Handoko Handoko; Rio Yunanto
Journal of Renewable Engineering Vol. 3 No. 4 (2026): JORE - August
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/8h6yam07

Abstract

The rapid deployment of reinforcement learning (RL) agents in critical national infrastructure has outpaced the governance instruments designed to hold them accountable, creating a widening gap between algorithmic autonomy and sovereign oversight. This article proposes a Three-Layer Cyber-AI Governance Framework that integrates the technical, organizational, and regulatory dimensions of accountability for RL systems operating within national data sovereignty regimes. Using a systematic literature review guided by PRISMA 2020 procedures, twenty-five peer-reviewed and preprint sources published between 2021 and 2026 were analyzed through thematic synthesis to identify recurring governance constructs across cybersecurity, AI ethics, and data-sovereignty scholarship. The synthesis reveals three interdependent layers: an Algorithmic Layer governing reward design, explainability, and adversarial robustness; an Organizational Layer governing human oversight, audit trails, and incident reporting; and a Sovereign-Regulatory Layer governing data localization, cross-border data flow, and international cooperation. The proposed framework departs from existing layered models by embedding a continuous feedback loop that links real-time algorithmic telemetry to national regulatory review, closing the accountability gap that single-layer or purely technical frameworks leave open. The article concludes that accountable reinforcement learning under conditions of national data sovereignty requires coordinated, multi-layer instruments rather than isolated technical fixes, and it outlines an agenda for empirical validation of the framework across diverse regulatory contexts.   
Artificial Intelligence Optimization in Renewable Energy Systems Rahmad Surya Hadi Saputra
Journal of Renewable Engineering Vol. 3 No. 4 (2026): JORE - August
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/wth1bt02

Abstract

The accelerating global transition toward decarbonized power systems has positioned renewable energy—particularly solar and wind as the backbone of future electricity supply, yet the inherent variability of these resources continues to challenge grid reliability and economic viability. Artificial intelligence (AI) has emerged as a decisive enabler for overcoming this variability by improving forecasting precision, enabling adaptive control, and supporting predictive maintenance across renewable energy assets. This article synthesizes findings from twenty-five peer-reviewed studies published between 2021 and 2026 to examine how machine learning, deep learning, reinforcement learning, and metaheuristic algorithms are optimizing renewable energy systems. A structured literature review methodology was applied, covering identification, screening, eligibility, and thematic synthesis stages. The findings indicate that deep learning architectures such as long short-term memory networks and convolutional neural networks substantially reduce forecasting error, while reinforcement learning and IoT-integrated frameworks improve real-time dispatch and resource allocation. Predictive maintenance applications report downtime reductions of up to twenty-five percent and inspection-time savings approaching ninety percent. The review further identifies a genuine novelty: existing studies remain fragmented across isolated technical domains, whereas an integrated, system-level framework linking forecasting, control, and maintenance intelligence is still underdeveloped. The article concludes by outlining research directions toward standardized datasets, explainable models, and federated architectures for scalable AI-enabled renewable energy optimization.
Sustainable Engineering Design for Circular Economy Applications Juniarmi
Journal of Renewable Engineering Vol. 3 No. 4 (2026): JORE - August
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/9ps88109

Abstract

The escalating depletion of finite natural resources and the mounting volume of industrial waste have intensified the need to embed circularity principles within engineering design practice. This article examines how sustainable engineering design can be systematically operationalized to support circular economy (CE) applications across manufacturing, construction, and materials-intensive sectors. Rather than relying on a systematic literature review, the study adopts a Design Science Research (DSR) approach combined with comparative case analysis, integrating life cycle assessment data, circular design strategies framed by the 9R hierarchy, and triple-bottom-line evaluation criteria. Findings indicate that closed-loop product architectures, modular and disassembly-oriented design, digital product passports, and material-substitution strategies substantially reduce embodied carbon, extend product service life, and recover residual material value, although implementation remains constrained by economic trade-offs, regulatory fragmentation, and limited design-stage circularity metrics. The novelty of this study lies in proposing an integrated engineering design framework that couples multi-criteria circularity assessment with digital traceability tools to bridge the persistent gap between conceptual circular economy principles and applied engineering practice. The framework offers engineers and policymakers a replicable decision-support structure for evaluating design alternatives across environmental, economic, and social dimensions, advancing the transition from theoretical circularity discourse toward measurable, design-stage sustainability outcomes.
Artificial Intelligence-Driven Predictive Maintenance for Smart Manufacturing Budi Witjaksana
Journal of Renewable Engineering Vol. 3 No. 4 (2026): JORE - August
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/99k6my71

Abstract

Artificial Intelligence-Driven Predictive Maintenance (AI-PdM) has become a strategic approach in smart manufacturing to reduce unscheduled downtime, lower maintenance costs, and extend the lifespan of production equipment. This article presents a conceptual review that synthesizes the latest developments in artificial intelligence algorithms—including classical machine learning, deep learning (CNN, LSTM, CNN-LSTM hybrid), federated learning, and explainable artificial intelligence (XAI) in the context of predictive maintenance in Industrial Internet of Things (IIoT) and digital twin environments. Unlike systematic literature reviews (SLRs) that employ formal selection and reporting protocols, this article uses a narrative synthesis approach and the development of a conceptual framework to map the AI-PdM workflow from sensor data acquisition to maintenance decision support system integration. The novelty of this review lies in the integration of three dimensions rarely discussed simultaneously within a single framework: predictive accuracy, model transparency through XAI, and data sovereignty through federated learning. The synthesis results show that deep learning-based hybrid models consistently outperform conventional methods on failure detection and remaining life estimation tasks, while the integration of XAI and federated learning is becoming an increasingly relevant direction to support the implementation of trustworthy, secure, and compliant data governance in distributed manufacturing environments  
Machine Learning-Based Structural Health Monitoring Systems Nicky Yongkimandalan
Journal of Renewable Engineering Vol. 3 No. 4 (2026): JORE - August
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/rg2xfk75

Abstract

Structural health monitoring (SHM) has shifted from periodic visual inspection and threshold-based signal processing toward data-driven diagnosis powered by machine learning (ML). This article synthesizes recent developments in ML-based SHM across civil, aerospace, and composite structures, focusing on sensing modalities, learning paradigms, and damage-related tasks (detection, localization, quantification, and prognosis). Drawing on a thematically organized body of twenty-five peer-reviewed sources published mainly between 2021 and 2026, the review traces the field's progression from supervised classifiers trained on labeled vibration or strain data toward unsupervised and physics-informed models capable of operating under scarce or unlabeled damage data and fluctuating environmental and operational conditions. The discussion highlights persistent challenges, including the scarcity of real damage-state data, sensitivity to environmental and operational variability, limited interpretability of deep architectures, and the difficulty of transferring models across structures. The article's novelty lies in proposing an integrated conceptual framework that links sensing, feature representation, learning strategy, and decision support into a single life-cycle pipeline, rather than treating these as isolated research strands. The synthesis further identifies digital twins, physics-informed learning, and explainable artificial intelligence as convergent directions for closing the gap between laboratory-validated ML-SHM models and trustworthy, field-deployable infrastructure management systems

Page 6 of 6 | Total Record : 57

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