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Ika Dewi Wijayanti
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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 192 Documents
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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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