Jurnal Mekintek : Jurnal Mekanikal, Energi, Industri, Dan Teknologi
Jurnal Mekintek : Jurnal Mekanikal, Energi, Industri, Dan Teknologi [p.ISSN 2086-7026] 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.
Development of a Smart Warehouse Framework Using Autonomous Mobile Robots for Warehouse 4.0 Applications
Warehouses are increasingly adopting automation technologies to improve operational efficiency, inventory accuracy, and process flexibility in response to the growing demands of modern logistics and supply chain systems. Among these technologies, Autonomous Mobile Robots (AMRs) have emerged as a key enabler of smart warehouse operations by providing autonomous transportation, intelligent navigation, and real-time decision-making capabilities. This study aims to develop a Smart Warehouse Framework using Autonomous Mobile Robots that integrates warehouse management, robot navigation, Internet of Things (IoT) devices, and real-time communication systems into a unified architecture. The proposed framework incorporates AMRs, RFID readers, barcode scanners, IoT sensors, Warehouse Management Systems (WMS), fleet management systems, and cloud-based databases to support intelligent warehouse operations. A simulation-based evaluation was conducted using realistic warehouse scenarios to assess the framework's performance based on operational and navigation metrics. The results indicate that the proposed framework significantly improves warehouse efficiency by reducing task completion time from 15 minutes to 7 minutes and decreasing average travel distance from 120 m to 65 m. Furthermore, warehouse throughput increased from 80 to 150 orders per day, while order-picking accuracy improved from 92% to 98%. Navigation performance also demonstrated high effectiveness, achieving mapping accuracy of 97.5%, localization accuracy of 98.7%, and obstacle avoidance success rates exceeding 98%. These findings demonstrate that the proposed Smart Warehouse Framework provides a scalable, intelligent, and efficient solution for Warehouse 4.0 implementation and supports the adoption of autonomous logistics systems in modern industrial environments.
Copyright (c) 2026 Kazuki Masato https://creativecommons.org/licenses/by-nc/4.0
Implementing Explainable Artificial Intelligence for Predictive Maintenance Decision Making in Industry 4.0
Predictive Maintenance (PdM) has become an important application of Artificial Intelligence (AI) in modern manufacturing environments, enabling organizations to predict equipment failures, optimize maintenance schedules, and improve operational efficiency. Despite their high predictive performance, many AI-based predictive maintenance models operate as black-box systems, limiting transparency and reducing user trust in maintenance recommendations. This study aims to implement Explainable Artificial Intelligence (XAI) techniques within predictive maintenance systems to improve model interpretability and support more transparent maintenance decision-making. Industrial equipment data collected from IoT sensors, including vibration, temperature, pressure, and runtime measurements, together with historical maintenance records, were analyzed using machine learning and deep learning models, namely Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM). Model performance was evaluated using Accuracy, Precision, Recall, and F1-score metrics, while explanation effectiveness was assessed through interpretability analysis and expert validation involving maintenance engineers, production managers, and reliability specialists. The results demonstrate that the proposed XAI-enabled predictive maintenance framework achieves high predictive performance, with the LSTM model obtaining the highest accuracy of 95.1%, outperforming RF and XGBoost models. Furthermore, SHAP and LIME successfully identified vibration and temperature as the most influential factors contributing to equipment failure predictions and provided understandable explanations for individual maintenance decisions. These findings suggest that integrating Explainable AI into predictive maintenance systems enhances model transparency, supports effective decision-making, and promotes the practical adoption of AI technologies in industrial environments.
Copyright (c) 2026 Ghazanfer Muhazzim, Mochtar Radhitya https://creativecommons.org/licenses/by-nc/4.0
Development of an Artificial Intelligence-Based Green Smart Manufacturing Framework for Sustainable Industrial Transformation
Manufacturing industries are facing increasing pressure to enhance productivity and operational efficiency while simultaneously reducing environmental impacts and supporting sustainable development goals. In response to these challenges, Artificial Intelligence (AI) has emerged as a transformative technology capable of enabling intelligent, data-driven, and environmentally responsible manufacturing systems. This study aims to develop a Green Smart Manufacturing Framework based on Artificial Intelligence that integrates sustainability principles with smart manufacturing technologies to support sustainable industrial transformation. The framework was developed using the Design Science Research (DSR) methodology, which involved problem identification, literature analysis, framework design, development, and expert-based validation. The findings identified three core dimensions of Green Smart Manufacturing, namely Green Manufacturing, Smart Manufacturing, and AI Capability, which were integrated into a unified framework architecture. The study contributes to the existing body of knowledge by extending Green Manufacturing theory through the integration of Artificial Intelligence and sustainability concepts within a comprehensive smart manufacturing architecture.
Copyright (c) 2026 Mufidah Nurul Afiya https://creativecommons.org/licenses/by-nc/4.0
Edge AI-Based Smart Factory Development for Carbon Emission Reduction
Manufacturing industries are facing increasing pressure to reduce carbon emissions while maintaining high levels of productivity and operational efficiency. In response to these challenges, Edge Artificial Intelligence (Edge AI) has emerged as a promising technology for enabling real-time analytics and intelligent decision-making within Smart Factory environments. This study aims to develop an Edge AI-based Smart Factory framework for monitoring, optimizing, and reducing industrial carbon emissions through intelligent energy management. The proposed framework integrates Industrial Internet of Things (IIoT) sensors, edge computing devices, artificial intelligence algorithms, and carbon monitoring modules to collect, process, and analyze manufacturing data in real time. Machine learning models, including Random Forest, XGBoost, and Long Short-Term Memory (LSTM), are deployed on edge devices to predict energy demand, detect operational inefficiencies, and optimize production activities. The framework is evaluated using energy efficiency, carbon reduction, operational performance, and AI model accuracy metrics. Experimental results demonstrate that the proposed system significantly improves operational efficiency, reducing energy consumption from 1000 kWh to 820 kWh and decreasing machine idle time from 18% to 7%. Furthermore, carbon emissions are reduced from 700 kg/day to 540 kg/day, representing a reduction of 22.9% compared to conventional factory operations. The LSTM model achieved the highest predictive accuracy of 95%, supporting effective real-time optimization and decision-making. These findings indicate that Edge AI can effectively support sustainable manufacturing by enabling intelligent energy management, real-time operational optimization, and carbon-aware production decisions, thereby contributing to the development of greener, more efficient, and more resilient Smart Factory ecosystems.
Copyright (c) 2026 Khaleed Sharim Rasyid https://creativecommons.org/licenses/by-nc/4.0
Digital Twin Integration Analysis for Overall Equipment Effectiveness (OEE) Improvement in Smart Manufacturing Environments
Manufacturing industries are increasingly adopting Digital Twin technology as part of Industry 4.0 initiatives to enhance operational efficiency, productivity, and competitiveness in rapidly evolving industrial environments. Despite advancements in manufacturing technologies, many organizations continue to face challenges such as unplanned machine downtime, inefficient maintenance practices, reduced production performance, and quality-related losses, all of which negatively affect Overall Equipment Effectiveness (OEE). This study aims to analyze the impact of Digital Twin integration on OEE improvement within manufacturing systems. A quantitative case-study approach was employed using machine operational data, production records, maintenance reports, and real-time sensor information collected from a manufacturing environment. The study compared OEE values before and after the implementation of Digital Twin technology through descriptive, comparative, and statistical performance analyses. The Digital Twin system integrated real-time monitoring, predictive maintenance, and process optimization capabilities by creating a virtual representation of physical production assets synchronized with operational data. The results revealed significant improvements across all OEE dimensions. Availability increased from 75% to 88% due to the reduction of unplanned downtime through predictive maintenance, while Performance improved from 82% to 91% as a result of enhanced process monitoring and operational optimization. Quality increased from 90% to 95% through improved process control and early detection of production anomalies. Consequently, overall OEE improved substantially from 55.35% to 76.08%. Furthermore, Digital Twin integration serves as a strategic enabler of smart manufacturing and Industry 4.0 transformation, contributing to increased productivity, operational excellence, and sustainable industrial development.
Copyright (c) 2026 Djehutihotep Wahibremesu https://creativecommons.org/licenses/by-nc/4.0