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INDONESIA
Jurnal Mekintek : Jurnal Mekanikal, Energi, Industri, Dan Teknologi
Published by Ihsa Institute
ISSN : 20867026     EISSN : 28087372     DOI : https://doi.org/10.35335/mekintek
Core Subject : Science, Engineering,
Aerospace Engineering Astronomy Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Engineering
Jurnal Mekintek : Jurnal Mekanikal, Energi, Industri, Dan Teknologi is a scientific journal that aims to participate in developing the scientific field of Mechanical, Energy, Industrial And Technology, contains the results of research and theoretical study from lecturers, researchers and industry practitioners. Jurnal Mekintek : Jurnal Mekanikal, Energi, Industri, Dan Teknologi is administered by the IHSA Institute. Jurnal Mekintek : Jurnal Mekanikal, Energi, Industri, Dan Teknologi published twice a year, in April and October. Editors receive scientific articles or papers containing the results of research, literature review, or review activity that is closely related to the field of Mechanical, Energy, Industrial And Technology.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 76 Documents
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Impact Analysis of Road Construction Projects on the Environment and Society Victor, Shinedy Louis
Jurnal Mekintek : Jurnal Mekanikal, Energi, Industri, Dan Teknologi Vol. 15 No. 2 (2024): October: Mechanical, Energy, Industrial and Technology
Publisher : IHSA Institute

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Abstract

This research explores the impact of road construction projects on the environment and society, aiming to provide a comprehensive analysis of both the direct and indirect consequences of infrastructure development. Through a detailed review of historical trends, case studies, and comparative analyses, the study highlights the significant environmental and social impacts associated with road construction. Key environmental concerns include habitat destruction, increased pollution, and noise pollution, while social impacts encompass displacement, changes in property values, and community disruption. The research reveals that while road construction projects can offer substantial benefits, such as improved connectivity and economic growth, they also pose challenges that need to be addressed. Advances in technology and methodology have enhanced our understanding of these impacts, leading to more effective mitigation strategies and better planning practices. However, the study identifies gaps in the implementation and effectiveness of Environmental and Social Impact Assessments (EIAs and SIAs) and emphasizes the need for rigorous monitoring and adaptive management. This research contributes valuable insights for policymakers, planners, and developers, guiding the creation of infrastructure that supports long-term sustainability and enhances quality of life.
Analysis of the Digital-Based Information System Learning Process on Student Readiness in the Digital Era Irwansyahputra, Muhammad; Syahputra, Muhammad Riza; Chandra, Suherman
Jurnal Mekintek : Jurnal Mekanikal, Energi, Industri, Dan Teknologi Vol. 16 No. 1 (2025): April: Mechanical, Energy, Industrial And Technology
Publisher : IHSA Institute

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Abstract

North Sumatra has several universities in various regions spread across 33 regencies/cities, where the number of students studying at several universities in North Sumatra according to the higher education database (PDDIKTI) is 130,182 students. As many as 25 percent of the number of students at several universities in North Sumatra who understand the use of information systems at the universities where they study, affect students' readiness to understand information system learning, which reduces students' understanding and knowledge, as well as their competence in knowing the contents of the material, understanding learning objectives, and understanding information system applications for decision making. This study aims to determine and analyze the extent to which the digital-based information system learning process influences students' readiness to understand learning using information systems in the digital era. The research method uses a quantitative descriptive method with a sampling technique using the accidental sampling method, and data collection techniques are carried out using observation, distributing questionnaires and documentation studies. The results of the study describe that the process of learning digital-based information systems has an effect on students' readiness in understanding learning information systems in the digital era, where students must be able to improve their knowledge, understanding and skills sufficiently by reading books and other literature, as well as increasing their applied knowledge about information systems in the digital era in order to be able and ready to improve their understanding of learning information systems.
Enhancing Energy Efficiency and Power Output in Synchronous Generators through Neodymium Magnet Integration Vehicle, Zacheus Jhope
Jurnal Mekintek : Jurnal Mekanikal, Energi, Industri, Dan Teknologi Vol. 16 No. 1 (2025): April: Mechanical, Energy, Industrial And Technology
Publisher : IHSA Institute

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Abstract

This research investigates the impact of integrating neodymium magnets into synchronous generators on energy efficiency and performance. Neodymium magnets, known for their high magnetic flux density, strong coercivity, and excellent thermal stability, offer potential enhancements over traditional ferrite magnets. Through a combination of experimental studies, computational simulations, and analytical evaluations, this study demonstrates that synchronous generators equipped with neodymium magnets achieve an 8-12% increase in energy efficiency and a 10-15% increase in power output. The superior magnetic properties of neodymium magnets facilitate more effective energy conversion and reduce operational losses, ensuring consistent performance even under challenging conditions. However, the adoption of neodymium magnets faces several challenges, including high initial costs, environmental impacts from rare-earth element extraction, technical design adaptations, and supply chain vulnerabilities. Despite these hurdles, the significant performance benefits of neodymium magnets present a compelling case for their integration into synchronous generators, promising more efficient, reliable, and sustainable power generation systems.
Optimization of Ilmenite Conversion to High-Purity Titanium Dioxide (TiO₂) in the Ternary System TiO₂-Fe₂O₃-Na₂O Listiawati, Listiawati; Astroha, Robie Wilson; Sunandar, Arman
Jurnal Mekintek : Jurnal Mekanikal, Energi, Industri, Dan Teknologi Vol. 16 No. 1 (2025): April: Mechanical, Energy, Industrial And Technology
Publisher : IHSA Institute

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Abstract

This research investigates the conversion of ilmenite (FeTiO₃) to titanium dioxide (TiO₂) in the ternary system TiO₂-Fe₂O₃-Na₂O, focusing on improving TiO₂ yield, purity, and quality for industrial applications. The study explores the effect of sodium oxide (Na₂O) as a flux to facilitate the removal of iron oxide (Fe₂O₃) impurities, which often degrade TiO₂'s optical and chemical properties. Through a series of experiments varying Na₂O concentrations and temperatures (900-1200°C), the research demonstrates that Na₂O enhances TiO₂ formation by promoting phase transitions and accelerating the separation of iron from titanium. Optimal conditions (5% Na₂O at 1100-1200°C) resulted in high-purity TiO₂ (up to 99%) with yields reaching 95%, while reducing iron oxide contamination. This study provides valuable insights into the reaction mechanisms and optimal parameters for producing high-quality TiO₂, with significant implications for its use in pigments, coatings, and advanced materials where purity and performance are critical. The findings contribute to more efficient, scalable methods for TiO₂ production from ilmenite, benefiting industries dependent on this essential material.
Performance Analysis of Self-Compacting Concrete in Earthquake-Resistant Buildings Ghaboussi, Demuth; lee, Kim Yeh
Jurnal Mekintek : Jurnal Mekanikal, Energi, Industri, Dan Teknologi Vol. 16 No. 1 (2025): April: Mechanical, Energy, Industrial And Technology
Publisher : IHSA Institute

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Abstract

This research investigates the performance of Self-Compacting Concrete (SCC) in earthquake-resistant buildings, comparing its behavior with conventional concrete under seismic conditions. SCC, known for its high flowability and ability to self-compact without the need for mechanical vibration, offers several advantages in construction, including reduced labor costs, faster construction timelines, and enhanced durability. The study explores the mechanical properties of SCC, such as compressive strength, shear strength, and flexural performance, and evaluates its resistance to cracking, permeability, and environmental degradation factors critical for earthquake resilience. Through experimental analysis and case studies, the research highlights SCC's superior performance in terms of structural integrity and long-term durability, particularly in regions prone to seismic activity. The findings demonstrate that SCC not only accelerates the construction process but also improves the overall safety and longevity of earthquake-resistant structures, making it a promising material for modern, sustainable construction practices. This study concludes that SCC offers significant benefits over conventional concrete, providing a cost-effective, durable, and environmentally friendly solution for earthquake-resistant building design and construction.
Analysis of the Impact of Implementation of Integrated Manufacturing Information Systems (MES) in Increasing Production Efficiency in the Electronics Industry Mizhir, Arham Bashir
Jurnal Mekintek : Jurnal Mekanikal, Energi, Industri, Dan Teknologi Vol. 16 No. 1 (2025): April: Mechanical, Energy, Industrial And Technology
Publisher : IHSA Institute

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Abstract

This research examines the impact of Manufacturing Execution System (MES) implementation on production efficiency in the electronics industry. As the electronics sector faces increasing demands for faster production cycles, higher quality standards, and cost optimization, the role of MES in streamlining production processes becomes critical. This study investigates how MES contributes to improving real-time data visibility, optimizing scheduling, enhancing quality control, and reducing downtime, all of which lead to higher production efficiency. The research also identifies the challenges associated with MES adoption, including high implementation costs, resistance to change, integration complexities, and data security concerns, while offering strategies for mitigating these issues. Through qualitative and quantitative analysis, the study highlights significant improvements in operational performance and competitive advantage post-MES adoption. The findings underscore the importance of MES as a strategic tool in the digital transformation of the electronics industry. This research contributes to the understanding of MES's role in manufacturing, offering practical insights for companies seeking to optimize their production processes and achieve sustainable growth. Additionally, the study provides a foundation for future research on MES integration with emerging technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) in manufacturing environments.
Analysis of Indonesian Industry Readiness for the Adoption of Autonomous Manufacturing Systems in the Era of Industry 4.0 Gibran Al Rafid
Jurnal Mekintek : Jurnal Mekanikal, Energi, Industri, Dan Teknologi Vol. 16 No. 2 (2025): October: Mechanical, Energy, Industrial and Technology
Publisher : IHSA Institute

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Abstract

The rapid advancement of Industry 4.0 technologies has accelerated the transition from conventional manufacturing systems toward Autonomous Manufacturing Systems (AMS), which integrate Artificial Intelligence (AI), Industrial Internet of Things (IIoT), robotics, cyber-physical systems, digital twins, and big data analytics to enable intelligent, self-optimizing, and highly efficient production processes. Given the increasing importance of autonomous manufacturing for enhancing industrial competitiveness, this study aims to assess the readiness of Indonesian manufacturing industries to adopt Autonomous Manufacturing Systems. A mixed-methods approach was employed, combining quantitative and qualitative techniques. Data were collected through questionnaires, interviews, observations, and secondary sources involving respondents from the automotive, electronics, food and beverage, and textile sectors. The collected data were analyzed using descriptive statistics, readiness index calculations, gap analysis, Structural Equation Modeling (SEM), thematic analysis, and content analysis. The results indicate that the overall readiness level of Indonesian manufacturing industries is moderate. However, human resource readiness and cybersecurity readiness remain significant challenges due to shortages of specialized talent, limited AI-related competencies, insufficient workforce training, and varying levels of cybersecurity preparedness. The study also found substantial disparities between large enterprises and SMEs in terms of technology adoption and resource availability. The study concludes that while Indonesian manufacturing industries have established a foundation for autonomous manufacturing adoption, further improvements in workforce development, technology integration, cybersecurity infrastructure, and policy support are required. To accelerate the transition toward autonomous manufacturing, collaborative efforts among industry, government, and educational institutions are essential to strengthen technological capabilities, develop skilled human resources, and create a supportive innovation ecosystem.
Analysis of the Influence of Artificial Intelligence on Predictive Maintenance Strategies in Production Machines Indra Siddhartha; Bhuvanesh Bhuvanesh; Bala Rudra
Jurnal Mekintek : Jurnal Mekanikal, Energi, Industri, Dan Teknologi Vol. 16 No. 2 (2025): October: Mechanical, Energy, Industrial and Technology
Publisher : IHSA Institute

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Abstract

The rapid advancement of Industry 4.0 and Industry 5.0 technologies has accelerated the adoption of Artificial Intelligence (AI) in manufacturing environments, particularly in predictive maintenance applications aimed at improving the reliability and performance of production machines. This study analyzes the influence of AI on predictive maintenance strategies and evaluates its contribution to enhancing maintenance effectiveness and operational performance in modern manufacturing systems. A Systematic Literature Review (SLR) approach was employed to synthesize findings from peer-reviewed publications indexed in major scientific databases, including Scopus, Web of Science, ScienceDirect, IEEE Xplore, and SpringerLink. Relevant studies published between 2020 and 2026 were selected and analyzed using descriptive, thematic, and comparative analytical techniques. The findings reveal that various AI technologies, including Machine Learning, Deep Learning, Artificial Neural Networks, Random Forest, Support Vector Machines, Reinforcement Learning, and Internet of Things (IoT)-enabled systems, are widely applied in predictive maintenance to support machine condition monitoring, fault diagnosis, and failure prediction. The results indicate that AI significantly improves prediction accuracy through early fault detection, reduces unexpected downtime by enabling proactive maintenance interventions, lowers maintenance costs through optimized resource allocation and spare-part utilization, and enhances operational efficiency by improving machine availability and production continuity. Furthermore, AI contributes to real-time monitoring, faster decision-making, and improved asset management. However, several implementation challenges remain, including data quality issues, sensor reliability concerns, integration with legacy systems, shortages of AI expertise, high implementation costs, cybersecurity risks, and data privacy concerns.
Analysis of the Effectiveness of Large Language Models in Industrial Knowledge Management: A Systematic Literature Review Jeronimo Malaquias
Jurnal Mekintek : Jurnal Mekanikal, Energi, Industri, Dan Teknologi Vol. 16 No. 2 (2025): October: Mechanical, Energy, Industrial and Technology
Publisher : IHSA Institute

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Abstract

The increasing adoption of Large Language Models (LLMs) has transformed the way organizations manage, access, and utilize knowledge in industrial environments. As industries continue to generate vast amounts of information, LLMs have emerged as powerful tools for enhancing knowledge management processes through advanced natural language understanding, information retrieval, and intelligent decision support capabilities. This study aims to analyze the effectiveness of LLMs in supporting industrial knowledge management and to evaluate their contributions, benefits, and challenges across organizational contexts. A Systematic Literature Review (SLR) approach was employed to examine relevant studies published between 2020 and 2026, with data collected from major scientific databases, including Scopus, Web of Science, IEEE Xplore, and ScienceDirect. The selected literature was analyzed using thematic, content, and comparative analysis techniques to identify patterns, applications, and implementation outcomes. The findings indicate that LLMs significantly enhance knowledge creation through automated report generation, technical documentation support, and lessons-learned extraction. Furthermore, LLM adoption contributes to increased organizational productivity by reducing information search time, supporting decision-making, and improving employee access to critical knowledge resources. However, several challenges remain, including hallucination, data inconsistency, model bias, integration complexity, security and privacy concerns, and issues related to transparency, accountability, and explainability. To maximize their benefits, organizations should implement robust AI governance frameworks, adopt secure knowledge retrieval architectures, and invest in employee AI literacy and training programs. Future research should focus on real-world industrial evaluations, comparative analyses of LLM platforms, and long-term assessments of organizational impacts.
Analysis of AI-Agent Implementation in Industry 5.0 Production Optimization: A Systematic Literature Review Kim Rae Dawn
Jurnal Mekintek : Jurnal Mekanikal, Energi, Industri, Dan Teknologi Vol. 16 No. 2 (2025): October: Mechanical, Energy, Industrial and Technology
Publisher : IHSA Institute

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Abstract

Industry 5.0 represents the next evolution of manufacturing, emphasizing the integration of advanced technologies with human-centered, sustainable, and resilient production systems. This study aims to analyze the implementation of AI agents in Industry 5.0 production optimization and evaluate their contributions to manufacturing performance. A Systematic Literature Review (SLR) approach was employed to examine relevant studies published between 2020 and 2026, sourced from Scopus, Web of Science, IEEE Xplore, ScienceDirect, and SpringerLink. The selected literature was analyzed across three dimensions The findings indicate that various AI-agent technologies, including Intelligent Agents, Multi-Agent Systems (MAS), Reinforcement Learning Agents, and Generative AI Agents, are increasingly utilized in manufacturing environments. Their implementation has led to significant improvements in production scheduling through reduced scheduling conflicts and enhanced machine utilization, predictive maintenance through improved failure prediction and reduced downtime, quality control through intelligent defect detection and automated inspection, and resource allocation through optimized utilization of labor, machinery, materials, and energy. Furthermore, AI agents contribute substantially to the core pillars of Industry 5.0 by supporting human-centric manufacturing through decision assistance and workplace safety enhancement, promoting sustainability through waste reduction and energy optimization, and increasing operational resilience through adaptive responses to disruptions and demand fluctuations. Despite challenges related to data integration, implementation costs, workforce readiness, and ethical considerations, the overall findings demonstrate that AI agents are highly effective enablers of production optimization and intelligent manufacturing. Future research should focus on autonomous factory ecosystems, human–AI collaborative agents, Digital Twin–AI Agent integration, and Explainable AI frameworks to further advance Industry 5.0 adoption and manufacturing innovation.

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