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JMES: The International Journal of Mechanical Engineering and Sciences Editorial Office Departemen Teknik Mesin, ITS Kampus ITS Sukolilo Surabaya 60111 Building C, Floor 2 Indonesia
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JMES: The International Journal of Mechanical Engineering and Sciences
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : -     EISSN : 25807471     DOI : https://dx.doi.org/10.12962/j25807471
Core Subject : Science, Engineering,
Energy Materials Science & Nanotechnology Mechanical Engineering
JMES publishes high-quality original research articles, review articles, and letters to the editor that advance mechanical engineering and related disciplines. The journal provides an international platform for researchers, academics, and industry practitioners to disseminate scientifically rigorous, innovative, and application-oriented research, bridging fundamental science with real-world engineering solutions. JMES welcomes high-quality original research and review articles covering theoretical, experimental, and numerical aspects in the following areas: Thermal and Fluid Engineering, including heat transfer, fluid flow, and energy conversion. Manufacturing and Mechanical Design, focusing on manufacturing processes, product development, and mechanical systems. Materials and Structural Engineering, covering engineering materials, structural performance, reliability, and failure analysis. Computational Engineering, emphasizing modeling, simulation, optimization, and data-driven engineering methods. Sustainable Energy Systems, addressing renewable energy, energy efficiency, energy storage, and low-carbon engineering technologies.
Arjuna Subject : Teknik (semua kategori) - Teknik Mesin
Articles 5 Documents
 1 
Search results for , issue "vol 9 no 2 (2025)" : 5 Documents clear
Occupational Health and Safety Risk Assessment of Surabaya Pump Houses using the HIRARC (Hazard Identification, Risk Assessment, and Risk Control) Method Reza Aulia Akbar; Maria Anityasari; Renaldi Jafras Arriyanto; Ignatius Dixon Melchior; Andi Candra Septaprasetya; Tri Broto Santoso
JMES: The International Journal of Mechanical Engineering and Sciences Vol 9 No 2 (2025)
Publisher : LPPM, Institut Teknologi Sepuluh Nopember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v9i2.22624

Abstract

Heavy rainfall caused by climate change has led many Indonesian cities to build pump houses to remove excess water and prevent flooding. While pump performance is critical for protecting urban communities, the health and safety of pump house workers has received little attention. Because their work is seasonal, occupational health and safety (OHS) in pump houses is often overlooked,even though protecting workers is essential for ensuring reliable pump operations during emergencies. This study applies the HIRARC (Hazard Identification, Risk Assessment, and Risk Control) method, commonly used in manufacturing, to assess risks in pump house operations. Field data were collected from four representative pump houses in Surabaya, with findings intended for broader application to 61 pump houses in the city. The results highlight urgent actions needed to improve worker safety and propose practical measures to strengthen OHS practices. These findings provide valuable insights for other Indonesian cities seeking to enhance their safety culture and ensure the resilience of flood control infrastructure.
Development of a System and Deep Learning Method for Metal Surface Corrosion Detection and Evaluation in Industrial Equipment Mohammad Rizanto Juliarsyah; Irwanda Yuni Pungkiarto; Faradilla Fauziyah Risnawati; Khoirul Anwar; Dhia Fairuz Shabrina
JMES: The International Journal of Mechanical Engineering and Sciences Vol 9 No 2 (2025)
Publisher : LPPM, Institut Teknologi Sepuluh Nopember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v9i2.23189

Abstract

Corrosion inspection of industrial assets is still dominated by subjective and inconsistent visual inspections. This study develops and validates a deep learning-based corrosion area detection system on metal surfaces in the context of heavy equipment through a binary segmentation task (corrosion vs. non-corrosion). Three architectures were compared: UNet, VGG16–Random Forest, and VGG16–UNet, using 600 annotated images measuring 512 × 512 pixels taken under lighting conditions of 50–150 lux. The workflow included preprocessing, augmentation, training for 30, 50, and 100 epochs, and evaluation of accuracy, precision, recall, IoU/Jaccard, Dice, and confusion matrix per pixel (positive = corrosion). The results show that VGG16–UNet provides the best performance; in the 150 lux test, it achieved 98.96% accuracy, 0.9934 precision, and 0.994 recall, with good consistency across lighting variations and data scales. These findings confirm the effectiveness of a pre-trained encoder combined with skip connections to recover fine corrosion boundaries and produce reliable corrosion maps. The proposed approach has the potential to standardize the inspection process and accelerate decision-making in reliability-based maintenance practices.
Optimizing Motorcycle Combustion System for Carbon Monoxide Emission Reduction Using the Taguchi Method Lailatus Sa’diyah Yuniar Arifianti; Ata Syifa’ Nugraha; Sudirman Rizki Ariyanto; Muhammad Rasyid Ridho
JMES: The International Journal of Mechanical Engineering and Sciences Vol 9 No 2 (2025)
Publisher : LPPM, Institut Teknologi Sepuluh Nopember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v9i2.23192

Abstract

In Indonesia, the dominance of motorcycles as the primary mode of transportation has created a significant urban air quality crisis, largely driven by exhaust emissions. Carbon monoxide (CO), a key indicator of incomplete combustion, poses a serious risk to public health and the environment. While previous studies have examined engine parameters such as spark plugs, ignition coils, or fuel quality in isolation, this study addresses a critical gap by shifting from single-factor analysis to a holistic, multi-parameter optimization. This approach is unique in its application of the Taguchi Method to identify a robust, real-world solution specifically tailored to the Indonesian context. We systematically optimized a motorcycle’s combustion system by evaluating three key parameters—fuel type, spark plug type, and ignition coil—at three levels each. Using an L9 Orthogonal Array and a smaller-the-better Signal-to-Noise (S/N) ratio, we aimed to minimize CO emissions. The results identified an optimal configuration of Mobil-brand fuel, an NGK Iridium spark plug, and a Suzuki A100 coil, which achieved a 42.03% reduction in CO emissions compared to the standard setup. Analysis of Variance (ANOVA) confirmed that fuel quality is the overwhelmingly dominant factor, contributing nearly 90% to the outcome. These findings provide a practical, low-cost emission control strategy with direct policy relevance for Indonesia, offering a clear path for vehicle maintenance shops and owners to contribute to cleaner air and support sustainable transportation goals.
A Participatory Risk-Matrix Framework for User-Centered Validation of a Manual Standing Wheelchair Alief Wikarta
JMES: The International Journal of Mechanical Engineering and Sciences Vol 9 No 2 (2025)
Publisher : LPPM, Institut Teknologi Sepuluh Nopember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v9i2.23228

Abstract

This study presents a participatory, risk-based validation framework for a manually actuated standing wheelchair. The standing function offers both physical and psychosocial benefits, including greater independence, improved social interaction, and better access to vertical space. However, adoption of such devices remains limited, especially in low-resource settings, due to concerns about usability, comfort, and safety. Rather than emphasizing technical novelty, the contribution of this study lies in applying a user-centered risk-matrix approach to systematically translate stakeholder concerns into design priorities. Through engagement with eight stakeholders, including direct users and institutional representatives, the study collected qualitative feedback on user experience. This feedback was organized into eight thematic risk categories. Among them, stability during transitions and the level of physical effort required were identified as the most pressing concerns. Each risk type was then evaluated using a qualitative 5×5 matrix to assess its likelihood and potential impact. This structured process enabled the design team to prioritize and implement targeted improvements, effectively reducing the likelihood of tipping-related risks. However, physical accessibility, particularly for users with limited upper-body strength, remained a high, unmitigated risk due to inherent limitations of manual operation. The study highlights the importance of integrating structured risk analysis with real user input to inform assistive technology development that is not only functional, but also contextually responsive.
Machine Learning-Based Prediction of Diesel Engine Health Using Operational Parameters: Comparison of SVM and BPNN Models Mohammad Khoirul Effendi; D. Mohakul
JMES: The International Journal of Mechanical Engineering and Sciences Vol 9 No 2 (2025)
Publisher : LPPM, Institut Teknologi Sepuluh Nopember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v9i2.10511

Abstract

Condition-based maintenance (CBM) is crucial for enhancing the reliability of diesel engines. This study evaluates the effectiveness of support vector machine (SVM) and backpropagation neural network (BPNN) in predicting engine faults using operational parameters, such as engine RPM, lubricating oil pressure, fuel pressure, coolant pressure, oil temperature, and coolant temperature. Unlike previous research, this study validates engine condition labels based on standardized operational parameter thresholds, ensuring a more reliable and realistic representation of data. Statistical analyses using Spearman correlation and ANOVA deviance reveal that engine RPM and coolant temperature are significant predictors of engine health (p < 0.05). The findings show a notable difference in performance between the two classification models assessed. The Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel achieved an accuracy of 85.46%. However, the BPNN, configured with a [2-3-3-2] layer architecture and utilizing the tansig activation function, significantly outperformed the SVM, achieving an accuracy of 97.16%. These results suggest that the BPNN is more adept at capturing nonlinear patterns and providing more accurate predictions. Overall, this study underscores the importance of integrating domain-based data validation with machine learning techniques to develop reliable predictive maintenance systems.

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