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International Journal of Industrial Innovation and Mechanical Engineering
Published by Asosiasi Riset Ilmu Teknik Indonesia
ISSN : 30474507     EISSN : 30474515     DOI : 10.61132
Core Subject : Science, Engineering,
Astronomy Computer Science & IT Engineering
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 Listrik dan Elektro
Articles 55 Documents
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Legal and Human Resource Frameworks for Autonomous Vessel Operations: Regulatory Compliance and Seafarer Workforce Transition in Indonesian Archipelagic Waters Tata Heru Prabawa
International Journal of Industrial Innovation and Mechanical Engineering Vol. 3 No. 1 (2026): February: International Journal of Industrial Innovation and Mechanical Enginee
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijiime.v3i1.370

Abstract

This research investigates integrated legal-human resource frameworks for autonomous vessel operations in Indonesian archipelagic waters, addressing regulatory compliance gaps and seafarer workforce transition challenges. Through qualitative analysis involving 38 stakeholders including maritime lawyers, regulatory officials, ship operators, seafarer unions, training institutions, and autonomous technology developers, this study examines how existing maritime legal frameworks prove inadequate for unmanned operations while workforce displacement threatens 150,000+ Indonesian maritime workers. Results demonstrate that successful autonomous vessel adoption requires coordinated legal-HR approaches addressing liability allocation (achieving 75-85% clarity through multi-party frameworks), competency certification for remote operators (reducing training gaps by 60-70%), career transition pathways (enabling 55-65% workforce adaptation), and regulatory harmonization (improving compliance efficiency by 45-60%). Key barriers include UNCLOS Article 94 incompatibility, insurance unavailability, seafarer resistance, and jurisdictional fragmentation. Findings reveal that archipelagic contexts demand unique legal-HR solutions integrating traditional maritime rights, hybrid operational modes, and just transition principles. This research contributes frameworks enabling Indonesia to proactively shape autonomous vessel regulations protecting both technological innovation and maritime workforce interests during critical technology transition.
Intelligent Cooling System Design for Main Ship Engines in Tropical Waters R. Herlan Guntoro; Pargaulan Dwikora Simanjuntak
International Journal of Industrial Innovation and Mechanical Engineering Vol. 3 No. 1 (2026): February: International Journal of Industrial Innovation and Mechanical Enginee
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijiime.v3i1.371

Abstract

This research investigates intelligent cooling system design for main ship engines operating in tropical waters, integrating advanced machinery engineering with human factors to address thermal management challenges affecting engine performance, reliability, and crew operational effectiveness. Tropical maritime environments impose severe cooling demands through elevated seawater temperatures (28-32°C), high ambient conditions (28-35°C), and accelerated biofouling, reducing conventional cooling system effectiveness by 15-25% while increasing maintenance burdens and operational risks. Through qualitative analysis involving marine engineers, chief engineers with tropical operational experience, cooling system manufacturers, naval architects, automation specialists, and maritime training institutions, this study examines how intelligent cooling systems incorporating variable-speed pumps, adaptive control algorithms, predictive maintenance, and crew-centered interfaces can optimize thermal management while supporting effective human-machine collaboration. Results demonstrate that intelligent systems can reduce cooling energy consumption by 20-35%, improve temperature stability by 50-65%, extend maintenance intervals by 40-80%, and enhance crew situational awareness through intuitive monitoring interfaces, while requiring comprehensive training programs developing technical understanding and operational competencies. Key implementation challenges include control system complexity, sensor reliability in harsh marine environments, integration with existing engine management platforms, crew competency development requirements, and lifecycle cost justification. Findings reveal that successful intelligent cooling system implementation requires holistic sociotechnical approach addressing machinery engineering optimization, automation technology deployment, and human capability development through coordinated design and training strategies. This research contributes to marine engineering literature by providing integrated frameworks for intelligent system design incorporating machinery performance, automation capabilities, and human factors supporting operational excellence in tropical maritime operations.
Biomass-Derived Surface Engineering of AISI 1020 Steel for Electromedical Applications Robittah, Ahmad; Akbar Hariyono, Muhammad; Sabitah, A'yan; Achmadi Achmadi; Kusuma Wardani, Ika
International Journal of Industrial Innovation and Mechanical Engineering Vol. 3 No. 1 (2026): February: International Journal of Industrial Innovation and Mechanical Enginee
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijiime.v3i1.387

Abstract

This study investigates biomass-derived surface engineering of AISI 1020 steel for electromedical applications using galam wood charcoal and chicken bone waste as carburizing media. Surface modification is required to improve the mechanical performance of low-carbon steel, particularly in applications that demand high wear resistance and long-term durability. A pack carburizing approach was applied using various ratios of biomass-derived media at a treatment temperature of 800 °C for 2 hours. Chemical composition was analyzed using Optical Emission Spectroscopy (OES), surface hardness was evaluated using Micro Vickers hardness testing, and microstructural characteristics were observed using optical microscopy. The results show a significant increase in surface carbon content with increasing fractions of chicken bone powder, indicating its effectiveness as a carbon donor and diffusion promoter. The surface hardness increased from approximately 150 HV in the untreated condition to a maximum of about 860 HV in the treated specimen. Microstructural observations revealed the formation of a distinct carburized layer with increasing thickness and uniformity, consistent with enhanced carbon diffusion and surface strengthening. These findings demonstrate that biomass-derived surface engineering provides an effective and sustainable approach for improving the surface properties of low-carbon steel. The proposed method offers strong potential for environmentally friendly manufacturing of durable and reliable electromedical components.
AI-Based Vision Inspection System for Automated Defect Detection in Additive Manufacturing Processes Using Deep Learning and Transfer Learning Approaches
International Journal of Industrial Innovation and Mechanical Engineering Vol. 1 No. 2 (2024): May: International Journal of Industrial Innovation and Mechanical Engineering
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijiime.v1i2.394

Abstract

Background: Additive manufacturing (AM) requires reliable and efficient defect detection mechanisms to ensure structural integrity and product quality, yet conventional inspection approaches remain time-consuming and often unsuitable for real-time industrial deployment. Objective: This study aims to develop and experimentally validate an artificial intelligence based vision inspection system capable of accurately detecting surface defects in AM components. Methods: A Convolutional Neural Network (CNN) architecture utilizing pretrained backbones (ResNet and EfficientNet) was implemented with a transfer learning strategy and data augmentation techniques. High-resolution AM surface images representing porosity, cracks, and layer misalignment were used for training and evaluation. Model performance was assessed using Accuracy, Precision, Recall, F1-score, and mean Average Precision (mAP), and comparative benchmarking was conducted against traditional machine learning models such as Support Vector Machine and Random Forest. Results: The proposed CNN-based models significantly outperformed conventional approaches, achieving up to 95.1% Accuracy and 92.8% mAP. The EfficientNet backbone demonstrated superior generalization capability, particularly in balancing Precision and Recall, indicating robust defect detection performance across multiple categories. These findings confirm that AI-driven inspection frameworks provide scalable and reliable quality assurance solutions for advanced manufacturing environments.
Autonomous Mobile Robot Navigation Optimization in Dynamic Warehouse Environments Using Reinforcement Learning and Sensor Fusion Techniques
International Journal of Industrial Innovation and Mechanical Engineering Vol. 1 No. 2 (2024): May: International Journal of Industrial Innovation and Mechanical Engineering
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijiime.v1i2.395

Abstract

Background: The rapid growth of warehouse automation and autonomous mobile robots has increased the need for adaptive navigation systems capable of operating safely and efficiently in dynamic industrial environments. Classical path planning algorithms such as A* and RRT perform well in structured settings but exhibit limitations when handling moving obstacles and environmental uncertainty. Objective: This study aims to develop and evaluate a reinforcement learning based navigation framework integrated with sensor fusion to improve path efficiency, collision avoidance, and robustness in dynamic warehouse scenarios. Method: An experimental research design was implemented combining high-fidelity simulation and real-world warehouse prototype testing. Deep Q-Network and Proximal Policy Optimization models were developed and trained using multi-sensor inputs from LiDAR, camera, and inertial measurement units. Performance was evaluated using path efficiency, collision rate, computational cost, and robustness metrics, with benchmarking against classical algorithms. Results: The results demonstrate that the Proximal Policy Optimization model achieved the highest path efficiency and lowest collision rate while maintaining stable computational performance under dynamic conditions. Reinforcement learning models significantly outperformed classical planners in adaptability and robustness, confirming their suitability for scalable industrial warehouse automation.
Digital Twin Driven Real Time Performance Optimization of Smart Factory Production Systems Using Edge Computing and Industrial Internet of Things Architecture
International Journal of Industrial Innovation and Mechanical Engineering Vol. 1 No. 2 (2024): May: International Journal of Industrial Innovation and Mechanical Engineering
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijiime.v1i2.396

Abstract

Background: The rapid advancement of Industry 4.0 has accelerated the integration of digital technologies such as the Industrial Internet of Things (IIoT), edge computing, and Digital Twin systems in smart manufacturing environments. However, many existing implementations remain fragmented and heavily dependent on centralized cloud infrastructures, resulting in latency constraints, limited scalability, and suboptimal real-time decision making. Objective: This study aims to develop and validate an integrated edge based Digital Twin optimization framework that combines IIoT sensing, hybrid edge cloud architecture, and reinforcement learning based adaptive control. Methods: The research adopts a multi phase design consisting of framework development, simulation based validation, and industrial pilot implementation. The proposed system integrates real time data acquisition, localized edge processing, Digital Twin synchronization, and intelligent optimization mechanisms to enhance operational efficiency. Results: The findings demonstrate significant performance improvements compared to conventional cloud based systems, including substantial latency reduction, increased production throughput, reduced downtime, and improved energy efficiency. Scalability and robustness testing further confirm that distributed edge intelligence enhances system resilience under increased workloads and network disruptions. These results indicate that integrating edge computing with Digital Twin modeling and reinforcement learning provides a scalable, responsive, and energy efficient solution for next-generation smart factories.
Experimental Investigation of Green Hydrogen Integration into Industrial Thermal Systems for Sustainable and Low Carbon Manufacturing Applications
International Journal of Industrial Innovation and Mechanical Engineering Vol. 1 No. 2 (2024): May: International Journal of Industrial Innovation and Mechanical Engineering
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijiime.v1i2.397

Abstract

Background: The global energy transition requires low-carbon solutions that can be integrated into existing thermal systems without drastic infrastructure changes. Hydrogen blending in conventional combustion systems has emerged as a promising pathway to reduce carbon emissions while maintaining operational flexibility. Objective: This study aims to experimentally evaluate the effect of hydrogen blending ratios (0–100% by volume) on thermal efficiency, CO₂ emissions, and NOx emissions, and to determine the optimal blending range based on technical and economic feasibility. Methods: An experimental thermal system prototype was developed and tested under controlled conditions with three repetitions per operating point. Performance parameters included combustion temperature, fuel consumption rate, and thermal efficiency, while emissions of CO₂ and NOx were measured using a calibrated gas analyzer. Data were analyzed using descriptive statistics, one-way ANOVA at a 0.05 significance level, confidence interval estimation, and linear regression to examine the relationship between hydrogen fraction and emission reduction. Results: The findings indicate that increasing hydrogen fraction significantly improves thermal efficiency, reaching 87.5% at 100% hydrogen, while CO₂ emissions decrease linearly to zero. However, NOx emissions increase with higher hydrogen content due to elevated combustion temperatures. Statistical analysis confirms that hydrogen ratio has a significant effect on efficiency and emissions, with a strong linear correlation between hydrogen fraction and CO₂ reduction. A blending range of 40–60% hydrogen provides the most balanced performance in terms of efficiency improvement, emission reduction, and cost feasibility.
Smart Composite Materials with Embedded Sensors for Structural Health Monitoring in High Performance Mechanical Engineering Applications
International Journal of Industrial Innovation and Mechanical Engineering Vol. 1 No. 2 (2024): May: International Journal of Industrial Innovation and Mechanical Engineering
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijiime.v1i2.398

Abstract

Background: Structural Health Monitoring plays a critical role in ensuring the safety, reliability, and sustainability of high performance composite structures used in aerospace, civil infrastructure, and mechanical systems. Conventional externally mounted sensors often face challenges related to environmental interference, maintenance complexity, and long term stability. Objective: This study aims to develop and validate an integrated smart composite monitoring system with embedded sensing capabilities that enhances damage detection accuracy and operational durability under varying mechanical stress conditions. Method: Smart composite specimens were fabricated by embedding fiber optic and piezoelectric sensors within fiber reinforced polymer laminates, followed by tensile, fatigue, and vibration testing. Signal processing techniques including time frequency analysis were applied to extract damage sensitive features, which were then classified using machine learning algorithms to distinguish healthy and damaged structural states. Results: The experimental findings demonstrate high damage detection capability, stable sensor performance under cyclic loading, improved reliability compared to conventional monitoring approaches, and consistent monitoring accuracy throughout the fatigue life of the specimens. The integration of embedded sensing and data driven analytics significantly enhances structural response interpretation and supports predictive maintenance strategies.
Design and Performance Evaluation of a Wet Cell HHO Generator as a Fuel Supplement for a Four-Stroke 125 cc Gasoline Engine Moh. Ali Sidik; Komarudin Komarudin; Denny Prumanto; Muhammad Iqbal Rasyid Ramadhan; Andi Mamonto
International Journal of Industrial Innovation and Mechanical Engineering Vol. 3 No. 3 (2026): August: International Journal of Industrial Innovation and Mechanical Engineeri
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijiime.v3i3.419

Abstract

The increasing use of gasoline-powered motorcycles contributes significantly to fuel consumption and exhaust emissions, particularly carbon monoxide (CO) and hydrocarbons (HC). One approach to improving combustion efficiency is the utilization of oxyhydrogen (HHO) gas as a supplementary fuel. This study aimed to design and evaluate the performance of a wet cell HHO generator as a fuel supplement for a 125 cc four-stroke gasoline engine. The developed system employed an 11-plate Stainless Steel 316L wet cell reactor with a 1.5 mm electrode gap and a 2% potassium hydroxide (KOH) electrolyte solution. The HHO generator was operated at a constant current of 2.5 A. Experimental testing included HHO production rate measurement, chassis dynamometer testing, specific fuel consumption (SFC) evaluation, and exhaust emission analysis under standard and HHO-assisted operating conditions. The functional test results showed that the reactor produced HHO gas at a stable average rate of 43.88 ml/min. Performance testing indicated that HHO supplementation increased average engine torque by 20.39% and average power output by 17.31%. In addition, the average SFC was reduced by 46.5%, indicating improved fuel utilization efficiency. Exhaust emission measurements revealed a reduction in average CO emissions from 2.01% to 0.56% and HC emissions from 376 ppm to 228 ppm. These findings demonstrate that the proposed wet cell HHO generator operated reliably and effectively enhanced engine performance, improved fuel economy, and reduced exhaust emissions in a 125 cc four-stroke gasoline engine.
Design of a 50 Wp Off-Grid Solar Power Plant Using a Linear Actuator Solar Tracker System Muhammad Fajry Setiawan; Maulana Adi Prakoso; Komarudin Komarudin; Erfiana Wahyuningasih
International Journal of Industrial Innovation and Mechanical Engineering Vol. 3 No. 3 (2026): August: International Journal of Industrial Innovation and Mechanical Engineeri
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijiime.v3i3.420

Abstract

Conventional solar power plants (PLTS) generally use fixed-angle solar panels that follow the roof inclination, limiting their ability to capture maximum solar radiation throughout the day, particularly during the afternoon when the panels are no longer perpendicular to the sun. To overcome this limitation, this study proposes the design of a 50 Wp off-grid photovoltaic system equipped with a linear actuator-based solar tracking mechanism and a portable support frame. The research aims to develop an alternative renewable energy system capable of automatically adjusting the panel orientation to maximize solar energy absorption, improve electrical output, and accelerate battery charging. A quantitative experimental approach was employed by comparing the performance of the PLTS before and after the implementation of the solar tracker. System performance was evaluated using a Seaward photovoltaic tester and a clamp ammeter to measure voltage and current output. The results indicate that the solar panel absorbs radiant energy at a rate of 242.352 J/s, of which approximately 20% is converted into electrical energy (48.4704 W), while the remaining 80% is dissipated as heat. The developed system can operate independently for up to three hours without grid support and requires approximately 2.5 hours to fully charge the battery. Mechanical analysis confirms that the linear actuator safely withstands the applied load of 74 N, well below its allowable limit of 5,528 N. These findings demonstrate the feasibility of the proposed design as an efficient, reliable, and portable off-grid renewable energy solution.

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