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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 5 Documents
 1 
Search results for , issue "Vol. 3 No. 2 (2026): JORE - April" : 5 Documents clear
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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