cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Vivien Suphandani Djanali
Contact Email
jmes@its.ac.id
Phone
+62315922941
Journal Mail Official
jmes@its.ac.id
Editorial Address
JMES The International Journal of Mechanical Engineering and Sciences Editorial Office Jurusan Teknik Mesin, ITS Kampus ITS Sukolilo Surabaya 60111 Building C, Floor 2 Indonesia
Location
Kota surabaya,
Jawa timur
INDONESIA
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
Topics covered by JMES include most topics related to mechanical sciences including energy conversion (wind, turbine, and power plant), mechanical structure and design (solid mechanics, machine design), manufacturing (welding, industrial robotics, metal forming), advanced materials (composites, nanotube, metal foam, ceramics, polymer), metallurgy (corrosion, non-destructive testing, heat treatment, metal casting), heat transfer, fluid mechanics, thermodynamics, mechatronics and controls, advanced energy storage and devices (fuel cell, electric vehicle, battery), numerical modelling (FEM, BEM).
Arjuna Subject : Teknik (semua kategori) - Teknik Mesin
Articles 5 Documents
 1 
Search results for , issue "Vol 9, No 2 (2025)" : 5 Documents clear
A Participatory Risk-Matrix Framework for User-Centered Validation of a Manual Standing Wheelchair Wikarta, Alief; Nurirawan, Rizkhi
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.
Intelligent Fault Prediction in Diesel Engines: A Comparative Study of SVM and BPNN for Condition-Based Maintenance Nurdin, Fadli; Effendi, Mohammad Khoirul; Mohakul, D
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.v9i2.22724

Abstract

This study discusses the application of Support Vector Machine (SVM) and Backpropagation Neural Network (BPNN) in predicting diesel engine health based on operational data that has been relabeled using K-Means Clustering. Two types of SVM kernels were tested, namely Radial Basis Function (RBF) and Sigmoid, with various parameter combinations. The results indicate that SVM with a Sigmoid kernel achieved an accuracy of 94.06% but was less sensitive in detecting unhealthy engine conditions. In comparison, the BPNN method with a three-hidden-layer configuration (1-2-1 neurons) and the tansig activation function demonstrated superior performance, achieving an accuracy of 97.13%, MSE of 0.03, recall of 94%, precision of 100%, and an F1-score of 97%. These results confirm that BPNN outperforms SVM in capturing complex data patterns and is more accurate in detecting unhealthy engine conditions. Furthermore, dataset relabeling significantly improved prediction accuracy from 72.3% to 97.13%, emphasizing the importance of data balance in modeling. Overall, this study demonstrates that BPNN with an optimal configuration is more effective in predicting diesel engine health than SVM, making it a more reliable approach for engine condition monitoring.Keywords: Diesel Engine; Machine Health Prediction; Support Vector Machine; Backpropagation Neural Network; Condition-Based Maintenance; Artificial Intelligence
Occupational Health and Safety Risk Assessment of Surabaya Pump Houses using the HIRARC (Hazard Identification, Risk Assessment, and Risk Control) Method Akbar, Reza Aulia; Anityasari, Maria; Arriyanto, Renaldi Jafras; Melchior, Ignatius Dixon; Septaprasetya, Andi Candra; Santoso, Tri Broto
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

Anticipating the impact of heavy rain triggered by climate change, almost all cities in Indonesia have built pump houses to suck up rainwater and puddles on city streets. The performance of the pump to drain and prevent flooding is very important for the people in the city. Unfortunately, not enough attention has been given to ensuring the health and safety of workers in pump houses. The welfare of pump house workers tends to be neglected because the seasonal nature of rain always makes the health and safety of workers in pump houses not a priority. In fact, improving the health and safety of pump house workers will increase the readiness of pump houses to be operated on time when needed. This paper describes the application of the HIRARC (Hazard Identification, Risk Assessment, and Risk Control) method commonly used in manufacturing companies to identify, evaluate, and mitigate risks related to the health and safety of workers in pump houses. Samples were taken from 4 different pump houses in Surabaya City, which can then be applied to 61 other pump houses. The results of the study indicate that several actions must be taken immediately to ensure the health and safety of workers. The findings presented in this paper will be useful for other cities in Indonesia to improve the safety culture in the country. 
Optimizing Motorcycle Combustion System for Carbon Monoxide Emission Reduction Using the Taguchi Method Arifianti, Lailatus Sa’diyah Yuniar; Irawan, M. Bahrul Ilmi; Nugraha, Ata Syifa'; Ariyanto, Sudirman Rizki; Ridho, Muhammad Rasyid
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 like 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 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.
Development of a System and Deep Learning Method for Metal Surface Corrosion Detection and Evaluation in Industrial Equipment Juliarsyah, Mohammad Rizanto; Yuni Pungkiarto, Irwanda; Risnawati, Faradilla Fauziyah; Anwar, Khoirul; Shabrina, Dhia Fairuz
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.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 9, No 2 (2025) Vol 9, No 1 (2025) Vol 8, No 2 (2024) Vol 8, No 1 (2024) Vol 7, No 2 (2023) Vol 7, No 1 (2023) Vol 6, No 2 (2022) Vol 6, No 1 (2022) Vol 5, No 2 (2021) Vol 5, No 1 (2021) Vol 4, No 2 (2020) Vol 4, No 1 (2020) Vol 3, No 2 (2019) Vol 3, No 1 (2019) Vol 2, No 2 (2018) Vol 2, No 1 (2018) Vol 1, No 2 (2017) Vol 1, No 1 (2017) More Issue