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International Journal of Mechanical, Industrial and Control Systems Engineering
Published by Asosiasi Riset Ilmu Teknik Indonesia
ISSN : 30474558     EISSN : 30474566     DOI : 10.61132
Core Subject : Science, Engineering,
Computer Science & IT Engineering
open research journal of the Engineering Science Clump. The fields of study in this journal include the sub-groups of Civil Engineering and Spatial Planning, Engineering, Electrical and Computer Engineering, Earth and Marine Engineering
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 45 Documents
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Improving Passenger Satisfaction through Six Sigma: An Analysis of BRT Trans Jateng Corridor 7 Primasanti, Yunita; Wahyu, Indah; Nugrahadi, Bekti
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 2 No. 3 (2025): September :IJMICSE: International Journal of Mechanical, Industrial and Control
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v2i3.352

Abstract

Bus Rapid Transit (BRT) systems play a crucial role in providing efficient and sustainable urban mobility, yet service quality often faces challenges that lead to passenger dissatisfaction. This study focuses on BRT Trans Jateng Corridor 7, which has received a considerable number of complaints regarding service reliability, comfort, and punctuality. The main objective of this research is to improve passenger satisfaction by applying the Six Sigma methodology to identify, analyze, and reduce the root causes of service-related complaints. Using the DMAIC (Define, Measure, Analyze, Improve, Control) framework, data were collected through complaint reports, passenger surveys, and operational performance indicators. Statistical analysis and cause-effect diagrams were employed to pinpoint critical factors influencing complaints, including bus frequency, driver performance, ticketing efficiency, and infrastructure support. The findings indicate that the application of Six Sigma significantly reduced recurring complaints, particularly in the areas of schedule adherence and service responsiveness, leading to measurable improvements in overall passenger satisfaction. The synthesis of results demonstrates that structured quality management approaches such as Six Sigma can effectively enhance public transportation service quality by providing systematic solutions to operational problems. In conclusion, this research highlights the practical benefits of Six Sigma in improving service performance and reducing passenger complaints, offering valuable insights for policymakers and transportation operators seeking to strengthen the quality and sustainability of public transit systems.
Development of an E-Commerce Website to Empower Kampung Lawas Maspati SMEs Mega Cattleya; Rizqi Novita Sari; Yekti Condro Winursito
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 2 No. 4 (2025): December :IJMICSE: International Journal of Mechanical, Industrial and Control
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v2i4.356

Abstract

The rapid advancement of digital technology has significantly influenced how small and medium enterprises (SMEs) connect with consumers. Kampung Lawas Maspati SMEs encounter obstacles in expanding their market due to limited online visibility and reliance on conventional sales approaches. This study aims to develop an e-commerce website that addresses these challenges by focusing on user-centered innovation. Using the Design Thinking method, which includes stages such as empathizing, defining, ideating, prototyping, and testing, user needs and challenges were explored through interviews, surveys, and observations to inform the website development process. The resulting platform is intuitive, responsive, and accessible, enabling SMEs to display their products, manage transactions, and engage with customers effectively. The application of this method not only improves market reach but also strengthens the competitiveness and digital literacy of Kampung Lawas Maspati SMEs. Evaluation results show a high level of consumer satisfaction, reaching 91.87%, indicating that the developed website effectively meets user expectations and supports SME digital transformation.
Effectiveness of Container Cargo Information Management System at Ports: Role of Competency Training, Shore-Based Personnel, and Technology at Indonesian Container Terminals Ferro Hidayah; Natanael Suranta; Yusuf Pria Utama
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 3 No. 1 (2026): March: International Journal of Mechanical, Industrial and Control Systems Engi
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v3i1.372

Abstract

This research investigates the effectiveness of container cargo information management systems at Indonesian ports, examining the critical roles of competency training, shore-based personnel capabilities, and technology adoption in determining system performance and operational outcomes. Container terminals increasingly rely on sophisticated information systems including Terminal Operating Systems (TOS), Port Community Systems (PCS), and Electronic Data Interchange (EDI) for coordinating complex cargo operations, yet system effectiveness depends not solely on technology sophistication but critically on personnel competency, training adequacy, and human-technology integration. Through qualitative analysis involving terminal operators, port information system managers, shore-based operational personnel, training coordinators, and technology providers, this study examines how the interplay between human capabilities and technological capabilities determines information system effectiveness. Results demonstrate that comprehensive competency training programs can improve system utilization effectiveness by 45-65%, reduce operational errors by 50-70%, and enhance productivity by 25-40% through better human-technology integration. Key challenges include inadequate training investment, high personnel turnover, technology complexity exceeding user capabilities, and organizational resistance to systematic training programs. Findings reveal that container terminal information system effectiveness depends fundamentally on sociotechnical system optimization addressing both technological infrastructure and human capability development through sustained competency building programs. This research contributes to port operations literature by providing evidence-based frameworks for human factors integration in port technology implementation.
Integration Model of Navigation and Port Information Systems with Blockchain Technology to Enhance Ship Operational Reliability Chanra Purnama; Larsen Barasa; Denny Fitrial
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 3 No. 1 (2026): March: International Journal of Mechanical, Industrial and Control Systems Engi
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v3i1.373

Abstract

This research investigates integration models combining navigation and port information systems through blockchain technology to enhance ship operational reliability, addressing critical challenges in data integrity, information sharing, and system coordination affecting maritime operations. Current maritime information systems operate in silos with limited interoperability, creating information asymmetries, coordination inefficiencies, and data integrity concerns that compromise operational decision-making and safety management. Through qualitative analysis involving ship operators, port authorities, navigation system providers, blockchain specialists, and maritime regulators, this study examines how distributed ledger technology can enable secure, transparent, and tamper-proof information integration across navigation and port systems. Results demonstrate that blockchain-based integration can improve data integrity assurance by 70-90%, reduce coordination time by 40-60%, enhance operational transparency by 55-75%, and decrease documentation errors by 50-70% through immutable records, smart contracts, and decentralized consensus mechanisms. Key implementation challenges include technical complexity, computational requirements, regulatory uncertainty, organizational adoption resistance, and scalability limitations. Findings reveal that blockchain represents transformative enabling technology for maritime digital ecosystems requiring trusted information exchange among multiple stakeholders without centralized intermediaries. This research contributes to maritime information systems literature by providing practical frameworks for blockchain implementation supporting operational reliability enhancement.
Climate Change Adaptation Frameworks for Port Infrastructure and Workforce Safety: Integrated Risk Management and Human Resource Resilience in Indonesian Maritime Operations Rosna Yuherlina Siahaan
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 3 No. 1 (2026): March: International Journal of Mechanical, Industrial and Control Systems Engi
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v3i1.374

Abstract

This research investigates climate change adaptation frameworks for Indonesian port infrastructure and workforce safety through integrated risk management approaches addressing physical facility resilience and human resource protection. Through qualitative analysis involving 37 stakeholders including port authorities, terminal operators, marine engineers, climate scientists, occupational health specialists, and port workers, this study examines how climate threats including sea level rise, extreme weather, flooding, and heat stress affect both port operations and worker safety requiring coordinated adaptation strategies. Results demonstrate that integrated frameworks can reduce climate-related operational disruptions by 50-70%, decrease worker heat illness by 60-80%, improve emergency response effectiveness by 55-75%, and enhance infrastructure resilience by 45-65% when combining physical hardening with workforce protection measures. Key challenges include immediate infrastructure damage (ports already experiencing 3-8 annual flooding shutdowns), worker heat illness epidemic (150+ cases in 2023 with 300% increase), investment decision urgency ($15-25 billion infrastructure commitments 2024-2030), and organizational coordination across fragmented stakeholders. Findings reveal that successful climate adaptation requires holistic sociotechnical approaches treating ports as integrated human-infrastructure systems where worker safety and facility resilience prove inseparable, supporting Indonesia's maritime economic security and coastal community welfare through comprehensive climate risk management.
A Fault Diagnosis and Intelligent Monitoring Framework Using Explainable Artificial Intelligence for Smart Industrial Machinery
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 2 No. 4 (2025): December :IJMICSE: International Journal of Mechanical, Industrial and Control
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v2i4.405

Abstract

Background: The development of industrial technology in the Industry 4.0 era has encouraged the implementation of intelligent monitoring systems to improve machine reliability and operational efficiency. However, machine fault diagnosis systems based on artificial intelligence often face limitations in terms of interpretability because the models used are complex and difficult to explain. Objective: This study aims to develop a deep learning-based industrial machine fault diagnosis system integrated with an Explainable Artificial Intelligence (XAI) approach to improve diagnostic accuracy while providing interpretable insights for users. Method: The research method involves collecting data from industrial machine sensors consisting of vibration signals, temperature measurements, and acoustic signals, followed by data preprocessing and feature extraction processes. The processed data are then used to train a deep learning-based diagnostic model, after which explainability methods such as SHAP or LIME are applied to analyze the contribution of each feature to the model’s prediction results. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results: The results indicate that the proposed deep learning model achieves better performance compared to conventional machine learning methods such as Support Vector Machine and Random Forest. Furthermore, the explainability analysis reveals that vibration amplitude, increases in machine component temperature, and anomalies in acoustic signals are the main factors influencing machine fault detection. Therefore, the proposed system not only improves the accuracy of machine fault diagnosis but also provides transparency in the decision-making process, thereby supporting the implementation of predictive maintenance in smart manufacturing environments.
Adaptive Human Robot Collaboration Model Using Computer Vision and Intelligent Control for Flexible Manufacturing Workstations
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 2 No. 4 (2025): December :IJMICSE: International Journal of Mechanical, Industrial and Control
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v2i3.406

Abstract

The rapid development of modern manufacturing technology has driven the emergence of human-robot collaboration (HRC) as part of the transformation toward a human-centric intelligent production system. In collaborative work environments, robots are not only required to work efficiently but also to interact safely and responsively with operators. However, most conventional industrial robot systems still use rigid motion controls and are unable to dynamically adapt to human activity around them.This research aims to develop a human-robot collaboration system by integrating computer vision technology to detect operator movement and applying adaptive control algorithms to the robot manipulator. The research methodology includes designing a collaborative workstation, implementing a computer vision-based motion detection system, developing an adaptive control algorithm, and evaluating system performance through various experimental scenarios. Evaluation parameters include task completion time, safe distance, and system response time.The results show that the developed system significantly improves the efficiency and safety of human-robot interaction compared to conventional systems, with shorter task times, optimal safe distances, and faster system response to operator movements.
Design of an Edge Computing Based Industrial Internet of Things Architecture for Real Time Predictive Maintenance in Advanced Manufacturing Systems
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 2 No. 4 (2025): December :IJMICSE: International Journal of Mechanical, Industrial and Control
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v2i4.407

Abstract

Background: The increasing complexity of industrial production systems requires machine condition monitoring solutions that are capable of operating in real time with high accuracy and responsiveness to support predictive maintenance strategies. Conventional cloud based monitoring systems often experience limitations such as high latency and dependence on stable network connectivity, which can delay decision making processes in critical industrial operations. Objective: This study aims to design and evaluate an Industrial Internet of Things (IIoT) architecture based on edge computing to improve the efficiency of industrial sensor data processing and accelerate anomaly detection in industrial machines. Method: The research adopts an experimental approach by designing a system architecture consisting of a sensor layer, edge computing layer, and cloud layer. Industrial sensors, including vibration, temperature, and current sensors, continuously collect machine operational data, which are then processed locally at the edge node using a machine learning based anomaly detection algorithm. System testing is conducted in a simulated manufacturing environment to evaluate performance based on latency, reliability, and detection accuracy. Results: The results indicate that edge based data processing significantly reduces latency compared with cloud-based processing and enables faster responses to machine condition changes. Additionally, the implemented anomaly detection algorithm achieves high accuracy in identifying abnormal sensor data patterns.
Energy Aware Reinforcement Learning Approach for Dynamic Production Scheduling Optimization in Sustainable Smart Manufacturing Environments
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 2 No. 4 (2025): December :IJMICSE: International Journal of Mechanical, Industrial and Control
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v2i4.408

Abstract

Background: The development of modern manufacturing systems requires production scheduling strategies that not only improve productivity but also optimize energy utilization. Multi-machine production systems with job-shop configurations exhibit high complexity due to dynamic interactions between machines, job queues, and varying processing times, making conventional scheduling methods less effective in handling changing operational conditions. Objective: This study aims to develop and evaluate a reinforcement learning based production scheduling approach to improve production efficiency while reducing energy consumption in multi-machine manufacturing systems. Methods: This research employs a job-shop based multi-machine production simulation model as the experimental environment. The scheduling problem is formulated as a Markov Decision Process, enabling the implementation of reinforcement learning algorithms, namely Q-learning and Deep Q-Network, to learn optimal scheduling policies through interaction with the simulation environment. Energy consumption parameters are incorporated into the reward function so that the learning agent can consider energy efficiency in the scheduling decision-making process. System performance is evaluated using three main metrics, namely energy consumption, throughput, and makespan. Results: The experimental results show that the reinforcement learning based scheduling approach achieves better performance compared to conventional scheduling methods, resulting in lower energy consumption, higher job completion rates, and shorter production completion times within the multi-machine manufacturing system.
Edge Computing Enabled Real Time Anomaly Detection Framework for Secure Industrial Cyber Physical Systems Using Lightweight Deep Neural Networks Deny Prasetyo; Suyahman Suyahman; Rosalina Yani Widiastuti; Mursalim Mursalim; Antoni Pribadi
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 1 No. 1 (2024): March: IJMICSE: International Journal of Mechanical, Industrial and Control Sys
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v1i1.399

Abstract

Cyber Physical Systems (CPS) are vital for managing and controlling critical infrastructures, such as industrial control systems, power grids, and transportation networks. These systems integrate digital and physical components, offering numerous benefits for industrial automation. However, the increasing interconnectivity of these systems has introduced new security vulnerabilities, particularly in anomaly detection and system reliability. This research aims to address these challenges by proposing an edge based anomaly detection framework that leverages lightweight deep learning models, specifically designed to operate efficiently on resource constrained edge devices. Literature Review: Previous studies have shown the effectiveness of anomaly detection in CPS, with traditional methods struggling to keep up with the complexity and scale of modern industrial environments. Machine learning and deep learning approaches, particularly hybrid models combining rule based systems and AI, have emerged as effective solutions for real time anomaly detection. Techniques such as model compression, quantization, and pruning are essential for adapting these models to resource limited edge devices while maintaining high detection accuracy and low latency. Materials and Method: The proposed framework integrates deep learning models such as Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM) networks, optimized for edge computing environments. The datasets used for training and testing include industrial network traffic and sensor anomaly datasets. Model optimization techniques like pruning and quantization were applied to reduce computational overhead and energy consumption on edge devices. Results and Discussion: The framework demonstrated high detection accuracy (AUC of 0.9720) with ultra low latency (0.0019 seconds training time), making it highly suitable for real time anomaly detection in CPS. Resource efficiency was achieved by optimizing the models for edge devices, reducing energy consumption while maintaining performance. The framework also significantly improved security by identifying anomalies early, preventing potential threats to critical infrastructures. Future directions include exploring federated learning to enhance privacy and data sharing across distributed devices.

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