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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 50 Documents
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Utilization of AR & VR for the Development of Safety Training and Risk Mitigation Anggraeni, Dwi Puspita
Journal of Renewable Engineering Vol. 3 No. 1 (2026): JORE - February
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/gwpr6197

Abstract

This study examines how Augmented Reality (AR) and Virtual Reality (VR) can be utilized to develop safer and more effective safety training while supporting performance assessment and behavioral change for risk mitigation in high-risk work environments. The background of the study arises from persistent workplace accidents in sectors such as construction, mining, healthcare, emergency response, and industrial processing, where conventional training methods often fail to provide realistic experiential preparation. This research employs a qualitative library research design by analyzing recent accredited journal studies on AR/VR applications in safety training. Data were collected through systematic documentation and analyzed using thematic content analysis focusing on immersive simulation, experiential learning, performance measurement, and behavioral outcomes. The findings show that AR/VR create zero-risk training environments that significantly improve hazard recognition, procedural skills, emergency readiness, and safety awareness compared to traditional approaches. In addition, AR/VR systems enable data-driven performance assessment by tracking user errors, response times, and compliance with safety protocols, fostering continuous improvement. The study concludes that AR and VR function as integrated systems for simulation, evaluation, and behavioral reinforcement, positioning them as strategic technologies for proactive risk mitigation in modern occupational safety management.
Performance and Emission Assessment of Tree-Based Biofuel Additives in Compression Ignition Engines: A Review Ilmi , Ilmi; Sitorus, Tulus Burhanuddin; Siagian, Parulian; Sihombing, Roland
Journal of Renewable Engineering Vol. 3 No. 1 (2026): JORE - February
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/89zh4557

Abstract

This study reviews the performance and emissions of compression ignition (CI) engines using Calophyllum inophyllum (CIME/tamanu)-based biofuel additives through a narrative review of the latest international literature. Inclusion criteria encompassed CI engine test studies reporting efficiency metrics (BTE, BSFC) and key emissions (CO, HC, NOx, smoke/PM) for CIME blends (B10–B100) both without and with additive/mitigation strategies. In general, compared to diesel, CIME reduced CO, HC, and smoke/PM, with a trade-off increase in NOx. The addition of oxygenated additives (e.g., n-pentanol, dimethyl carbonate) and ignition improvers (e.g., DTBP) tends to improve combustion quality, reduce BSFC, and suppress CO/HC; while the application of approximately 10% EGR effectively reduces NOx with a moderate penalty on HC/CO/smoke. Nano-additives (graphene/MWCNT) show potential for increasing BTE and reducing smoke, but present issues of dispersion stability and safety/environment. The most balanced performance generally occurs at low–medium blends (≈B10–B20) combined with oxygenated additives and EGR-based NOx control, accompanied by proper injection calibration. From a sustainability perspective, C. inophyllum—as a non-food source with high FFA pretreatment requirements—has the potential to support transportation decarbonization, although industrial-scale success depends on supply chains, policies, and LCA/TEA results. Further studies are recommended on real-world test cycles, long-term durability, aftertreatment compatibility, and comprehensive environmental assessment.
Improving Textile Production Efficiency Through the Implementation of Lean Manufacturing in the Weaving Department Irwanto, Miko Mei
Journal of Renewable Engineering Vol. 3 No. 1 (2026): JORE - February
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/17yqy036

Abstract

This study examines how a customized Lean Manufacturing approach can improve production efficiency in the weaving department of a textile company. Weaving operations frequently experience inefficiencies due to machine downtime, waiting time, excessive operator movement, and product defects, which create a gap between targeted and actual output. A quantitative case study design was applied using direct observation, time study, Value Stream Mapping, production records, and maintenance logs. Lean tools including 5S, Total Productive Maintenance, layout improvement, line balancing, poka yoke, and Kaizen were implemented in a pilot weaving area. The results indicate a significant reduction in machine downtime by 53 percent, waiting time by 59 percent, operator movement by 39 percent, and defect rate by 50 percent. Value Stream Mapping analysis further shows that non value added time decreased substantially while value added time remained stable, leading to a 24 percent reduction in total lead time and a 22 percent increase in daily production output. These findings confirm that Lean Manufacturing, when customized to the characteristics of weaving processes, effectively eliminates waste and enhances workflow. The study concludes that integrating Lean with maintenance and process standardization provides a practical strategy to bridge the gap between production targets and actual performance in textile weaving units.    
Development of High-Performance Engineered Textiles for Medical Applications: A Quality Function Deployment (QFD) Study Irwanto, Miko Mei; Pane, Akhmad Fauzi; Wijayanti, Atiek Ike
Journal of Renewable Engineering Vol. 3 No. 1 (2026): JORE - February
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/0jjcs864

Abstract

The rapid growth of biomedical applications and the increasing demand for advanced healthcare solutions have intensified the need for high-performance engineered textiles in medical contexts. These textiles must simultaneously fulfill stringent clinical, mechanical, biological, and regulatory requirements. This study aims to develop and analyze a Quality Function Deployment (QFD) framework to systematically translate clinical and user requirements into prioritized engineering specifications for medical textile development. A quantitative–descriptive approach was employed using stakeholder surveys, expert interviews, and literature analysis to identify the Voice of Customer (VoC). The House of Quality matrix was constructed to evaluate relationships between customer needs and technical characteristics. The results indicate that biocompatibility, mechanical durability, and antimicrobial performance are the highest-priority customer requirements. Correspondingly, fiber material composition, fabric structure, and surface functionalization emerged as the most critical technical characteristics. The discussion demonstrates that QFD effectively reduces overdesign, enhances cross-disciplinary alignment, and improves resource allocation in product development. In conclusion, QFD provides a structured and strategic framework for optimizing the development of high-performance medical textiles, ensuring alignment between clinical expectations and engineering feasibility while supporting innovation sustainability.  
Integration of Industry 5.0 Principles in Sustainable Manufacturing Systems: A Literature Review on the Role of Human–Machine Collaboration and Its Impact on Productivity Purnomo, Rahmat; Wedono, Bramono Wangsa
Journal of Renewable Engineering Vol. 3 No. 1 (2026): JORE - February
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/tavh6g94

Abstract

The transition from Industry 4.0 to Industry 5.0 represents a paradigm shift from technology-driven efficiency toward human-centric, sustainable, and resilient manufacturing systems. Human–machine collaboration is increasingly recognized as a core mechanism for achieving productivity while simultaneously addressing social and environmental objectives. This study aims to systematically review the integration of Industry 5.0 principles in sustainable manufacturing, with particular emphasis on the role of human–machine collaboration and its impact on productivity. A Systematic Literature Review (SLR) was conducted using peer-reviewed journal articles published between 2021 and 2025 from major academic databases. Thematic analysis was applied to synthesize key concepts related to human-centric smart manufacturing, human–robot collaboration, cyber-physical systems, digital twins, and sustainability performance indicators. The findings indicate that collaborative human–machine architectures enhance operational efficiency, quality consistency, adaptability, and resilience, while also supporting worker well-being, skill development, and environmental performance. However, gaps remain regarding long-term empirical validation, standardized sustainability measurement frameworks, and implementation in diverse industrial contexts. In conclusion, Industry 5.0 provides an integrative socio-technical framework in which human–machine collaboration functions as a strategic bridge between productivity enhancement and sustainable manufacturing performance.
Integrating Large Language Models into Decision Support Systems for Objective Student Competition Participant Selection Eka V. Dangkua; Fazrul Anugrah Sahi; Nikmasari Pakaya; Indhitya R. Padiku; Muchlis Polin; Rahmat Taufik R. L Bau
Journal of Renewable Engineering Vol. 3 No. 2 (2026): JORE - April
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/wfdsxn09

Abstract

The selection process for competition participants in the Informatics Engineering Department at Gorontalo State University is still carried out manually, potentially leading to subjectivity and suboptimal identification of deserving students. This study developed an artificial intelligence-based selection system using a Large Language Model (LLM) integrated with the Retrieval-Augmented Generation (RAG) approach. The developed system, named Scout, recommends competitions that match student profiles based on academic data, interests, experience, and achievements. The system evaluation used the Precision@K and Hit@K metrics to measure recommendation accuracy, and RAGAS to assess the quality of retrieval and chatbot responses. Test results showed that the Scout system obtained a Precision@3 score of 0.87 and a Faithfulness score of 0.91, indicating high recommendation relevance and factual consistency. Thus, the implementation of LLM and RAG has proven effective in increasing the objectivity and efficiency of the selection process and has the potential to become the basis for the development of an AI-based academic decision support system in higher education
Systematic Literature Review: The Implementation of Smart Manufacturing to Enhance Production Process Efficiency Taryana Taryana
Journal of Renewable Engineering Vol. 3 No. 2 (2026): JORE - April
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/ej870708

Abstract

The emergence of smart manufacturing in the Industry 4.0 era has transformed traditional production systems into interconnected, data-driven environments aimed at enhancing efficiency and flexibility. However, conventional manufacturing systems continue to face challenges such as planning errors, bottlenecks, machine downtime, and limited technological integration. This study aims to systematically analyze the implementation of smart manufacturing technologies in improving production process efficiency. The research employs a systematic literature review (SLR) with a qualitative approach, utilizing secondary data from peer-reviewed journals and academic publications. Data collection follows structured stages of identification, screening, eligibility, and inclusion, while data analysis is conducted through thematic and content analysis to identify patterns, technologies, and efficiency outcomes. The findings reveal that key technologies such as Cyber-Physical Systems (CPS), Internet of Things (IoT), Artificial Intelligence (AI), big data analytics, and digital twins significantly contribute to improving production efficiency through real-time monitoring, predictive maintenance, and optimized decision-making. These technologies effectively reduce downtime, minimize waste, and enhance overall system performance. In conclusion, smart manufacturing represents a transformative approach that addresses inefficiencies in conventional systems and supports sustainable industrial development through integrated and intelligent production processes.
Human-Centered Engineering: Integrating Human Factors in Modern Engineering System Design Dwiyanto, Dwiyanto
Journal of Renewable Engineering Vol. 3 No. 2 (2026): JORE - April
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/8vnwgp88

Abstract

This study explores the integration of human factors into modern engineering systems through a human-centered engineering approach that emphasizes safety, usability, and long-term socio-technical sustainability. The increasing complexity of engineering systems, driven by digitalization and automation, necessitates a shift from technology-centered design toward approaches that prioritize human interaction and well-being. The study employs a mixed-method approach, combining a systematic literature review, expert interviews, and case study analysis across industrial, healthcare, and digital system contexts. Data were analyzed using thematic analysis, content analysis, and comparative evaluation to identify key dimensions of human-centered engineering, including physical, cognitive, and organizational ergonomics, as well as Human-Centered Design (HCD) and Human Systems Integration (HSI). The results indicate that integrating human-centered components significantly enhances system performance, particularly in terms of safety, usability, efficiency, and adaptability. The discussion reveals that the synergy between human factors and digital technologies, such as human modeling and simulation, plays a critical role in optimizing system design. However, challenges remain in terms of late integration, interdisciplinary collaboration, and limited representation of human factors in digital engineering frameworks. In conclusion, the development of modern engineering systems requires a comprehensive human-centered strategy that aligns technological innovation with human needs to achieve resilient, efficient, and sustainable systems.
Machine Learning-Based Automation in Production Processes: Enhancing Efficiency and System Accuracy in Industry Amali, Amali; Tasya, Amalia
Journal of Renewable Engineering Vol. 3 No. 2 (2026): JORE - April
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/eqp79y32

Abstract

The integration of Machine Learning (ML) in production automation has become a key driver in transforming industrial systems into smart and adaptive manufacturing environments. This study aims to analyze the role of ML in improving efficiency and accuracy within production processes. The research employs a qualitative approach with a descriptive-analytical design, using library research and document analysis of reputable scientific sources. Data were analyzed through an interactive model consisting of data reduction, data display, and conclusion drawing. The findings reveal that ML significantly enhances operational efficiency through predictive maintenance, optimized scheduling, and real-time decision-making, while also improving accuracy in quality control through advanced algorithms such as deep learning, Support Vector Machines, and Artificial Neural Networks. Furthermore, ML enables process optimization by analyzing complex production variables and identifying optimal parameters. However, challenges such as data quality, system integration, and model interpretability remain critical barriers. The study concludes that a holistic integration of ML, supported by advanced technologies such as IIoT and Digital Twin, is essential for achieving higher efficiency, improved accuracy, and sustainable competitiveness in modern industrial systems.
Smart Manufacturing: Integration of Industry 4.0 Technologies to Enhance Engineering System Productivity Simon, Gusman; Wedono, Bramono Wangsa
Journal of Renewable Engineering Vol. 3 No. 2 (2026): JORE - April
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/f71t3f33

Abstract

The transformation of manufacturing systems in the era of Industry 4.0 has led to the emergence of smart manufacturing, which integrates advanced technologies such as Internet of Things (IoT), cyber-physical systems (CPS), artificial intelligence (AI), and digital twins to enhance engineering system productivity. This study aims to analyze how the strategic integration of these technologies improves productivity by addressing system complexity, integration challenges, and human factors. The research employs a qualitative approach using a systematic literature review (SLR) method, with data collected from reputable international journals published between 2020 and 2025. The analysis is conducted through thematic and comparative synthesis to identify key patterns and relationships. The findings indicate that the integration of Industry 4.0 technologies significantly improves operational performance, including increased efficiency, reduced downtime, enhanced product quality, and improved system responsiveness. However, challenges such as integration complexity, legacy systems, and workforce skill gaps remain critical barriers. The study concludes that a holistic and strategic integration approach, supported by strong data governance and workforce development, is essential to achieve sustainable productivity improvements in engineering systems.

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