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Engineering Science Letter
Published by Indonesian Institute of Science and Technology Research
ISSN : 29618924     EISSN : 2961872X     DOI : https://doi.org/10.56741/esl.v1i02
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology
Engineering Science Letter is an international peer-reviewed letter that welcomes short original research submissions on any branch of engineering, computer science, and technology, as well as their applications in industry, education, health, business, and other fields. Artificial intelligence, image processing, data mining, data science, bioinformatics, computational statistics, electrical engineering, electronics engineering, telecommunications, hardware systems, industrial automation, industrial engineering, fluids and physics engineering, mechanical engineering, chemical engineering, and their applications are among the engineering and computer science topics covered by the journal. All papers submitted will go through a peer-review process to ensure their quality. Submissions must contain original research and contributions to their field. The manuscript must adhere to the author’s guidelines and have never been published before.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 8 Documents
 1 
Search results for , issue "Vol. 5 No. 01 (2026): Engineering Science Letter" : 8 Documents clear
Modelling Optimisation of Distributed PV-Battery Charge and Discharge Modes Using Systems for Improved Sustainable Energy Management Baqaruzi, Syamsyarief; Mustaqim, Amrina; Muhtar, Ali; Rizky Hikmatullah, Muhammad; Fadhilah, Rahmat; Munandar, Andika; Kharisma Army, Edo; Wira Buana, Setiadi; Wahyudi, Rizqi; Rifqi Dwi S, Muhammad
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.001508

Abstract

This study examines the simulation of charge and discharge modes of lithium-ion batteries in a distributed photovoltaic system using MATLAB/ Simulink modeling. The objective is to analyze the integration of solar panels with battery-based energy storage systems to optimize performance and efficiency. The methodology involves mathematical modeling of photovoltaic cells based on p-n junctions, with key parameters such as temperature (15–30°C) and irradiance (1000 W/m²), along with the design of a Solar Charge Controller (SCC) to regulate energy flow. Simulations were conducted on four 150 W photovoltaic panels under varying environmental conditions, integrated with parallel-connected 12 V 250 Ah batteries. Results show a system efficiency of 87% at 25°C and 1000 W/m² irradiance, with panel output voltages aligning with mathematical equations (0.15 A error). Discharge mode analysis, accounting for system losses (inverter 5%, SCC 3%, wiring 2%), confirms the battery can supply a 5 Ω load for approximately 2.00 hours at 45% State of Charge (SOC), representing a 9.5% reduction from the ideal calculation. Simulations also compare SCC performance using DC and photovoltaic sources, demonstrating consistency in energy flow regulation. Validation results indicate the Simulink model’s accuracy in representing real-world characteristics, though MATLAB code simulations exhibit higher precision. The study highlights the importance of SCC control and SOC management to enhance battery lifespan and stability in renewable hybrid energy systems. Implications include potential applications.
Performance Evaluation of Machine Learning Algorithms for Supply Chain Data Classification Maniah
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.001524

Abstract

Forecasting systems that are data-driven are of great importance in streamlining industrial and business processes during the digital transformation age. Supply chain management (SCM) is among the most significant processes for enhancing operational efficiency and supporting strategic decision-making. This study seeks to evaluate the performance of two machine learning-based classification algorithms, namely Naive Bayes and the k-Nearest Neighbours (K-NN) algorithm, using data in the supply chain. Some of the most valuable operational attributes, including payment method, customer segment, shipment status, profit per transaction, and customer location, are stored in the database. The data were first cleaned and then normalised and label-encoded, after which they were split into training and test sets with a ratio of 80:20. The performance of the two algorithms was assessed using accuracy, precision, recall, and F1-score. The findings of the research indicate that Naive Bayes is the most promising algorithm; its accuracy and precision are 99.75%, and its recall rate is close to 100% in the majority of the classes. These findings show that Naive Bayes is a probabilistic algorithm that better fits the data distribution than a distance-based K-NN algorithm.
Impact of Sustainable Drainage with Porous Asphalt to Reduce Urban Flood Risk in Vulnerable Residential Areas Sriwati, Meny
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.001551

Abstract

Climate change and rapid urbanization in Makassar have increased the frequency of heavy rainfall, causing excessive runoff and flooding in densely populated areas. Conventional drainage systems are no longer able to accommodate the increased water volume, thus creating an urgent need for sustainable engineering solutions. The objectives of this study were to test the effectiveness of porous asphalt in managing stormwater runoff and increasing infiltration, and to develop a sustainable drainage system model that suits the hydrological, technical, and socio-economic conditions of tropical urban areas. The method used was a quantitative experiment with a hydrological and environmental engineering approach. The study sample included twenty field test locations in flood-prone areas of Makassar. Data were collected through field infiltration measurements, SWMM simulations, and laboratory asphalt porosity tests. Analysis was performed using multiple linear regression and hydrological model validation. The results showed that porous asphalt was able to increase infiltration capacity from 210 to 340 mm/hour and reduce surface runoff volume by 40.7 percent. The inundation depth was reduced by half from the initial condition, and the drainage system efficiency increased from an index of 3.2 to 4.3. The correlation coefficient between porosity and infiltration reached 0.79 (p < 0.01), indicating a strong positive relationship. These findings indicate that the application of porous asphalt effectively reduces flood risk while improving environmental quality. In conclusion, porous asphalt is a feasible technical and ecological solution for water runoff management in tropical areas. Therefore, a sustainable drainage system based on porous asphalt can be a strategic component in adaptive urban development to climate change.
Techno-Economic Analysis of Small-Scale Reverse Osmosis Desalination for Likupang Tourism Area Nauli, Maria Anindita; Harwin; Durry, Kezia Rambu
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.001698

Abstract

Likupang, a Super Priority Tourism Destination in Indonesia, faces significant challenges in securing a reliable and affordable fresh water supply, currently depending on expensive trucked-in water. This study evaluates the techno-economic feasibility of a small-scale Reverse Osmosis (RO) desalination system to address this issue. Using DuPont’s WAVE Water Treatment Design software, three distinct operational scenarios (A = 50, B = 80, and C = 90 m³/day capacities) were designed and simulated to accommodate the fluctuating water demand characteristic of a tourism area. The technical analysis identified the 80 m³/day demand-responsive scenario as the most energy-efficient, with a Specific Energy Consumption (SEC) of 3.73 kWh/m³. Meanwhile, the economic evaluation, based on the Levelized Cost of Water (LCOW), determined that the most cost-effective strategy is Scenario A with an LCOW of 60,766 IDR/m³. This cost is significantly lower than the current market price of trucked-in water, demonstrating that small-scale RO desalination is a viable and economically competitive solution to support sustainable tourism development in Likupang.
Analysis of Patterns and Drag of Tandem Minibus Models in Various Configurations Rauf, Wawan; Boli, Rahmad Hidayat; Ishak, Sahional; Talango, Novriyanti
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.001961

Abstract

The flow through three mini buses arranged in tandem is one type of fluid flow in infrastructure and transportation engineering. The characteristics of the flow pattern formed in each vehicle have an influence on the flow pattern formed in other vehicles. The purpose of this study is to analyze the drag coefficient and characteristics of the flow pattern in models in various configurations and distances between models. The test was conducted by applying experimental methods and numerical computation to obtain the characteristics of the flow pattern. Specifically to obtain the results of the drag coefficient, the method used was only numerical computation. The results of the study showed that the distance between the M/D models influenced the flow pattern formed in all configurations. The smallest drag coefficient for each configuration 1 Cd = 1.43285 at M/D = 0.03, Configuration 2 Cd = 0.80341 at M/D = 0.01, and configuration 3 Cd = 0.77911 at M/D = 0.03 as well as the model with the lowest drag compared to all distances in configuration 1 and configuration 2.
Natural Disaster Classification Using MobileNet with Transfer Learning Mulyani, Asri; Kurniadi, Dede; Suciyana, Gina
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.001682

Abstract

Natural disasters are events caused by natural phenomena that cause massive damage and pose a threat to human safety. Based on EM-DAT data (2000–2025), there have been more than 10,000 global disasters, resulting in millions of casualties and trillions of US dollars in losses. Notably, 2024 saw US$320 billion in losses due to extreme weather. This condition emphasizes the importance of an accurate disaster classification system for mitigation and rapid response. This study aims to develop a natural disaster image classification model using the Convolutional Neural Network (CNN) method with a Transfer Learning approach using the MobileNetV2 architecture, which is known to be efficient and lightweight. This study employs the SEMMA (Sample, Explore, Modify, Model, Assess) methodology, beginning with sampling, which involves collecting image data from various open sources, such as Kaggle and previous literature. The data is then processed through the selection, cleaning, and normalization stages. Data exploration is conducted to understand class distribution and detect data imbalance. To overcome this problem, the Synthetic Minority Over-sampling Technique (SMOTE) and image data augmentation (such as rotation, flipping, and contrast adjustment) were used to enrich the training data variation. The pre-trained MobileNetV2 model was then retrained using the modified data, with adjustments to hyperparameters to achieve optimal performance. The evaluation was conducted using various metrics, namely accuracy, precision, recall, F1-score, confusion matrix, and AUC-ROC curve. The results demonstrate that the combination of Transfer Learning, data augmentation, and SMOTE can enhance model performance, achieving an accuracy of up to 99% and an AUC-ROC above 0.99 on public test data. Additionally, testing on 26 private test images yielded an accuracy of 92.31%, with 24 of the 26 images classified correctly. These findings confirm that the combination of MobileNetV2, augmentation, and SMOTE effectively improves multi-class classification performance on natural disaster images. Furthermore, the use of a relatively lightweight model makes this system more efficient to implement on devices with limited resources, thereby supporting disaster mitigation and rapid response efforts.
Performance-Efficiency Tradeoff Analysis of YOLOv8 Variants for Real-Time Multiclass Vehicle Detection in High-Density Traffic Kurniadi, Dede; Mulyani, Asri; Nuraisah, Nuraisah
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.001702

Abstract

The growing number of vehicles in Indonesia increases the need for an efficient and reliable traffic monitoring system. In Garut Regency, traffic monitoring is still carried out manually without the support of artificial intelligence, thus limiting the effectiveness of real-time traffic analysis. This study develops and evaluates a CCTV image-based vehicle classification model using YOLOv8 with a focus on application in real-world traffic conditions. The development process follows the Machine Learning Life Cycle (MLLC) stages, including data acquisition, preprocessing, training, and model evaluation. The dataset comprises 1,200 CCTV traffic images from 10 locations in Garut Regency, supplemented by 7,426 additional images from the Roboflow platform to enhance the diversity of viewpoints and visual conditions. To address class imbalance, an undersampling technique is applied so that each vehicle category, motorcycle, car, truck, bus, and public transportation, has a balanced number of instances. Three YOLOv8 variants, namely Nano, Small, and Medium, are trained and evaluated using two testing schemes: a 70:20:10 data split and a 5-fold cross-validation method. Performance evaluation was conducted using the mean Average Precision (mAP), precision, recall, and inference speed metrics. The experimental results show that YOLOv8m with the 5-Fold Cross Validation scheme produces the best performance with mAP@50 of 0.947, precision of 0.932, and recall of 0.883, while YOLOv8n excels in terms of inference speed with an average of ±8.77 ms/frame. These findings suggest that the selection of YOLOv8 variants should consider the balance between accuracy and computational efficiency and confirm the potential of YOLOv8 as an initial component of an automated CCTV-based traffic monitoring system in real-world environments with limited resources.
Comparative Thermal Analysis of Single and Double Channel Cold Plates for LiFePO4 Battery Modules Yamin, Mohamad; Gufroni, Aldi
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.

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