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All Journal Jurnal Ilmiah Informatika Komputer Jurnal Ilmiah Teknologi dan Rekayasa Jurnal Teknologi Jurnal Ilmiah KOMPUTASI Briliant: Jurnal Riset dan Konseptual Journal of Energy, Mechanical, Material and Manufacturing Engineering Jurnal Teknik dan Science (JTS) International Journal of Innovation in Mechanical Engineering and Advanced Materials Engineering Science Letter Journal of Novel Engineering Science and Technology Bincang Sains dan Teknologi Jurnal Polimesin
Mohamad Yamin
Universitas Gunadarma
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation

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Neural network approach for predicting aerodynamic performance of NACA airfoil at low Reynolds number Mohamad Yamin; Zaid Al Kahfi Ramadhan
Jurnal POLIMESIN Vol 20, No 2 (2022): August
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v20i2.3065

Abstract

In designing and developing airfoils, confirmation of proper design performance under various flow conditions is vital. Experimental studies using wind tunnels or numerical simulations can often utilize. In some cases, numerical studies have a weakness in computational time. This study focuses on predicting the drag coefficient of the airfoil using the CNN machine learning architecture. Starting with a numerical simulation of 500 types of NACA airfoils with a Reynolds number of 4000 using XLRF5 software to obtain image data, lift and drag coefficients. The training, test, and validation dataset uses numerical simulation results as labels. ReLU is the activation function used in this study, with Adam optimizer and MSE loss function. It achieved a relative error of 8% in predicting the drag coefficient. With the results obtained, aircraft designers can use the method to predict the drag coefficient value from various geometries.
Numerical study of downwash flow on rice plant protection drone with computational fluid dynamics method Mohamad Yamin; Muhammad Zidan Alfasha
Jurnal Polimesin Vol 22, No 4 (2024): August
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v22i4.5036

Abstract

Unnamed Unmanned Aerial Vehicles (UAVs) are increasingly being utilized in various industries, including agriculture, to support the growing demand for food. UAVs streamline work processes and are particularly useful in the spraying method for plant protection. This study aims to analyze the characteristics of the downwash flow, which are influenced by factors such as flight altitude, airfoil profile, and the flying speed of the drone. Unlike previous studies that used 6-blade UAVs, this research focused on a 4-blade configuration. The study employed Computational Fluid Dynamics (CFD) to analyze drone geometry and input boundary conditions based on environmental factors. The drone's flying altitude significantly impacted downwash flow, particularly concerning In Ground Effect (IGE) and Out of Ground Effect (OGE) conditions. Unlike previous research, this study considered the airfoil profile of the propeller, which, along with the drag and lift coefficients from the airfoil geometry, affected the downwash flow. The drone's flying speed, related to the relative wind speed around its working area, also influenced pressure distribution and downwash flow speed. These factors significantly impacted downwash flow and determined the distribution of plant protection droplets on the rice field. The results indicated that increasing flight altitude reduced the ground effect, affecting the quadcopter's downwash. Similarly, flight speed had a similar effect on downwash as altitude. Based on these findings, the study recommended a flight altitude of 2 m and a speed of 2 m/s for optimal downwash and proper distribution of plant protection.
Comparative Thermal Analysis of Single and Double Channel Cold Plates for LiFePO4 Battery Modules Mohamad Yamin; Aldi Gufroni
Engineering Science Letter Vol. 5 No. 01 (2026): Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.esl.002023

Abstract

Li-ion batteries provide many advantages and are essential components of energy-storage systems for electric automobiles. A crucial aspect of battery operation is the maintenance of optimal temperature levels, which necessitate the implementation of a robust battery thermal management system. This study assessed the efficacy of two cold-plate configurations, a single parallel channel and a double parallel channel, in regulating the temperature of a 7 Ah LiFePO4 battery module comprising of three cells. Employing ANSYS 2023 R1 Academic License, a numerical analysis was performed to evaluate their performance. A battery discharge rate of 5C was used to investigate the changes in the mass flow rates ranging from 0.001 to 0.005 kg/s. The cooling fluid and ambient temperatures were maintained at 25°C. This study shows that double parallel-channel cold plates can be more effective than single parallel-channel cold plates in reducing battery module temperatures. Additionally, the use of double parallel-channel cold plates can result in a lower cooling fluid pressure drop. In addition, the cooling fluid used in the double parallel channel cold plate had a lower heat-transfer coefficient and Nusselt number.
The Effect of the Number of Fins on Rocket Aerodynamic Performance Using Computational Fluid Dynamics Mohamad Yamin; Nuril Hadi
Bincang Sains dan Teknologi Vol. 5 No. 01 (2026): Bincang Sains dan Teknologi
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/bst.v5i01.2026

Abstract

This study aims to analyze the effect of the number of fins on the aerodynamic characteristics of a rocket using the Computational Fluid Dynamics (CFD) method. Three rocket configurations with different numbers of fins (3, 4, and 6 straight fins) were modeled and simulated at a subsonic speed of Mach 0.6 with angles of attack ranging from 0° to 25°. The main aerodynamic parameters analyzed include the lift coefficient (Cl), drag coefficient (Cd), and moment coefficient (Cm). The simulation results show that increasing the number of fins tends to improve rocket stability and produces a more uniform pressure distribution. The six-fin configuration yields the highest Cl/Cd ratio, particularly at an angle of attack of 10°, indicating the best aerodynamic efficiency. In addition to aerodynamic performance, economic considerations are also an important factor; the use of six fins provides advantages in terms of stability, although it requires higher manufacturing costs compared to three or four fins. Overall, the six-fin configuration is recommended for rocket applications that req uire high stability and aerodynamic efficiency.
Ride Test on Vehicles Travelling Over Speed Bumps: Simulation with CarSim Software Adetia Lenahatu; Mohamad Yamin
International Journal of Innovation in Mechanical Engineering and Advanced Materials Vol. 6 No. 2 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijimeam.v6i2.26135

Abstract

This study explores the effects of different speed bump geometries—flat-topped, sinusoidal, and parabolic—on vehicle dynamics and ride comfort using CarSim simulations. The analysis focuses on key parameters such as vertical forces on the suspension, vertical acceleration, and the wheel surface adhesion index. The results show that flat-topped bumps generate the highest vertical forces, reaching peaks of up to 6,000 N on the front suspension, leading to increased discomfort. Sinusoidal bumps, in contrast, generate smoother transitions, with vertical forces peaking at approximately 3,500 N, improving ride comfort. At vehicle speeds of 30 km/h, the vertical forces on the suspension increase significantly, with flat-topped bumps reducing the wheel surface adhesion index to as low as 0.6, indicating a higher risk of wheel slip and compromised vehicle stability. In contrast, sinusoidal bumps maintain a more favorable adhesion index of 0.85 at similar speeds. These reductions in adhesion elevate the risk of loss of control, especially at higher speeds. The findings suggest that adaptive suspension systems, capable of adjusting damping and stiffness based on the bump geometry and vehicle speed, would enhance ride quality and stability. Additionally, smoother bump designs, such as sinusoidal profiles, are recommended to reduce the impact on vehicle dynamics, particularly in urban environments. These insights contribute to improving both vehicle design and road safety, ensuring safer and more comfortable driving experiences.
Handling and Stability Analysis of an Autonomous Vehicle Using Model Predictive Control in a CarSim–Simulink Co-Simulation Environment Mohamad Yamin; Mega Maulida Mumtaz; Riyan Firmansyah
International Journal of Innovation in Mechanical Engineering and Advanced Materials Vol. 7 No. 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijimeam.v7i2.31812

Abstract

Cars are a prevalent mode of transportation for both people and goods, with B-class hatchbacks being particularly popular in Indonesia. However, road traffic crashes remain a major concern, contributing millions of deaths annually, primarily due to human error. Autonomous vehicles offer a promising solution to mitigate these issues by reducing reliance on human control. In particular, Level 3 autonomous vehicles enhance road safety, enable independent mobility, reduce traffic congestion, and allow drivers to engage in non-driving tasks. This study proposes an autonomous vehicle model that employs a trajectory tracking approach using Model Predictive Control (MPC), a robust and widely adopted control strategy in autonomous systems. A three-degree-of-freedom (3-DOF) vehicle dynamic model was developed and analyzed through co-simulation using CarSim and Simulink to evaluate its performance during a double-lane change maneuver. The simulation results demonstrate that the vehicle accurately follows the reference trajectory and exhibits excellent dynamic performance. The roll angle remained consistently low, ranging between 0.024 and 0.026 radians—well below the rollover threshold of 0.14 radians—demonstrating strong roll stability. The slip angle varied between –0.013 and 0.0135 radians, nearly 12 times lower than the critical limit, indicating optimal traction and directional control. Lateral acceleration ranged from –3.59 m/s² to 3.41 m/s², and yaw rate remained within –7.78°/s to 7.25°/s, both well within safe operational bounds. These findings confirm that the proposed MPC-based control framework enables precise path tracking, robust stability, and reliable handling performance in dynamic driving scenarios.
Comparative Evaluation of Zero-Shot Vision-Language Models and YOLO for Image-Based Weld Defect Severity Assessment Muhammad A’Zom Ar-Rabaqi; Mohamad Yamin
Bincang Sains dan Teknologi Vol. 5 No. 02 (2026): Bincang Sains dan Teknologi
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/bst.v5i02.2565

Abstract

Automated visual inspection of welded joints increasingly uses supervised deep-learning models, particularly YOLO object detectors. Although effective for defect localization, these approaches require domain-specific annotated data and provide limited engineering-oriented textual explanations. This study compared supervised YOLO-based pipelines with zero-shot Vision-Language Models (VLMs) for image-based weld-defect severity assessment. A public Welding Defect–Object Detection dataset containing 1,983 images was used. A four-level ordinal severity rubric was developed as an AWS D1.1-informed engineering heuristic rather than a direct implementation of AWS acceptance criteria because the source images lacked consistent physical scale calibration. Two supervised detectors, YOLOv8n and YOLO26n, and two zero-shot VLMs, NVIDIA Nemotron and Meta Llama 3.2 Vision-90B, were evaluated. All pipelines processed 401 validation-and-test images, while primary comparison against independently rated human references used an adjudicated subset of 100 images. Human inter-rater agreement was high (Cohen’s κ = 0.839; weighted κ = 0.929). Nemotron achieved the highest accuracy (55.0%), followed by YOLOv8n (50.0%), YOLO26n (48.0%), and Llama Vision-90B (38.0%), and the highest Level-4 recall (0.871). Both YOLO pipelines showed zero recall for Level 2. Although unadjusted McNemar testing found p = 0.030 for Nemotron versus Llama Vision-90B, no pairwise comparison remained significant after Holm correction. VLM explanation quality was moderately associated with classification correctness (r = 0.533; p = 0.0001). None of the methods was sufficiently reliable for standalone engineering acceptance decisions, but their complementary failure patterns support VLMs as an auxiliary review layer within human-supervised weld inspection.
Co-Authors Adetia Lenahatu Ahmad Fakhri Giyats Aldi Gufroni Amar Ratnakar Naik Bayu Ramadhan Danny Setiawan Mega Maulida Mumtaz Muhamad Dewangga Muhammad A’Zom Ar-Rabaqi Muhammad Zidan Alfasha Nuril Hadi Riyan Firmansyah Sandya Gilang Samudra Satria Wijayandaru Silvia Casmadi Sri Poernomo Sari Sri Poernomosari Vivi Failawati Zaid Al Kahfi Ramadhan Zaid Al Kahfi Ramadhan