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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 84 Documents
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Performance-Efficiency Tradeoff Analysis of YOLOv8 Variants for Real-Time Multiclass Vehicle Detection in High-Density Traffic Dede Kurniadi; Asri Mulyani; 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 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.
Subject Area Classification of Journal Articles Based on Metadata Using Bag of Words and Naïve Bayes Ainunna’imah; Herman Yuliansyah; Imam Riadi
Engineering Science Letter Vol. 5 No. 02 (2026): In Press - 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.002041

Abstract

The rapid growth of scientific publications poses challenges in grouping journal articles based on subject area, especially when using metadata such as titles, abstracts, and keywords. However, differences in feature representation and classification algorithms often result in varying performance, requiring comparative studies to determine the optimal model combination. This study compares four combinations of subject area classification models, namely TF-IDF + Naïve Bayes, TF-IDF + Support Vector Machine, Bag-of-Words + Support Vector Machine, and Bag-of-Words + Naïve Bayes. The research process included text preprocessing, feature extraction, and testing using an 80% training and 20% testing data split scheme in five scenarios. The evaluation was performed using confusion matrices, accuracy, precision, recall, and F1-score. The experimental results showed variations in performance between models, with an average F1-score of 0.8103 for TF-IDF + Naïve Bayes, 0.8494 for TF-IDF + Support Vector Machine, 0.8297 for Bag-of-Words + Support Vector Machine, and 0.8335 for Bag-of-Words + Naïve Bayes as the best performance. These findings indicate that a word frequency-based approach combined with Naïve Bayes is effective for classifying journal article subject areas based on metadata, although challenges remain in subject areas with semantic proximity.
Development of a Non-Invasive Method for Monitoring of HV Circuit Breaker Switching Time Sagar Bhutada
Engineering Science Letter Vol. 5 No. 02 (2026): In Press - 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.002082

Abstract

Modern HV circuit breakers may be vulnerable to catastrophic failure as they are designed for higher stress than the earlier CB designs with multiple interrupters per pole. With the advantage of controlled switching, improved performance is obtained for dielectrically well-designed interrupters, which achieve a re-ignition-free window during opening of the CB, in turn minimizing the risk of nozzle puncture. On occasion, asset owners may wish to check whether the CB is performing satisfactorily and whether the controllers are providing reliable and repeatable stress control. Monitoring of voltage waveforms during switching using well-established offline diagnostic methods will provide information about small re-ignitions and re-strikes. However, waveform measurement at moderately high signal frequency would require a CB outage to connect specialized equipment. A non-invasive measurement technique devising re-striking voltage sensors has been developed by the authors to measure high-frequency voltage waveforms occurring during switching operations without the need for an outage. Results of tests performed in the laboratory and 245 kV substation illustrating the capability of this new method to detect re-ignitions are presented in this paper. The proposed diagnostic approach relies on parameters such as operating times, pre-strike characteristics, and restrike detection. Transient electromagnetic emissions have been identified as a promising means to evaluate the above parameters non-intrusively.

Page 9 of 9 | Total Record : 84

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