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INDONESIA
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Published by IAES Indonesia Section and Partners
ISSN : 20893272     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is a peer reviewed International Journal in English published four issues per year (March, June, September and December). The aim of Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the engineering of Telecommunication and Information Technology, Applied Computing & Computer, Instrumentation & Control, Electrical (Power), Electronics, and Informatics.
Arjuna Subject : -
Articles 825 Documents
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Resolution–Accuracy Trade-offs in UAV-Based Semantic Segmentation for Precision Agricultural Imagery Mohamed Tawhid Amin; Aziza Ibrahim Hussein; Mohamed Mourad Mabrook
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 2: June 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i2.6512

Abstract

The paper explores resolution-accuracy trade-offs in UAV-based semantic segmentation for precision agricultural imagery, which is an important challenge in achieving computational efficiency while maintaining satisfactory results for semantic segmentation. It is widely believed that higher resolution imagery leads to more accurate results, but it also raises processing expenses and restricts the ability to deploy in real-time systems with unmanned aerial vehicles (UAV). This study comprehensively analyzes the effect of the spatial resolution (also known as Ground Sampling Distance (GSD)) on segmentation performance on a range of agricultural anomaly types. The agriculture-vision dataset is used in experimental runs of three different GSDs (10 cm, 20 cm and 40 cm per pixel) with UAV-acquired images. The standard semantic segmentation metrics (mean Intersection over Union (mIoU), Dice coefficient, and computational time analysis) are adopted to evaluate several deep learning models such as U-Net, R2U-Net, U-Net3+, Attention U-Net and DeepLabV3+. The results show that there is no clear correlation between achieving higher spatial resolution and achieving better segmentation accuracy. Medium resolution imagery (20 cm/pixel) can produce similar or better results for large scale anomalies, including dryness and nutrient deficiency, and at significantly lower computational costs. On the other hand, fine-grained anomalies require more fine-grained resolution to better display the anomalies, which shows that the best resolution depends on the task. Further, multi-scale feature aggregation models have higher robustness to resolution degradation. The findings offer a practical understanding of designing the resolution-aware model, which could lead to more efficient use of UAV for resolution-aware deployment, flight planning, and the implementation of edge-AI in precision agriculture. The study provides a data-driven tool for optimizing the spatial resolution versus accuracy versus efficiency of real-world monitoring systems in agriculture.
Deep Learning-Based Pest Insect Detection for Autonomous Agricultural Systems: Development and Field-Testing of a Custom Convolutional Neural Network Architecture Selim Sürücü; Berk Küçük; Mahammad Karimzade
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 2: June 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i2.7672

Abstract

Global food security is under increasing threat from pest organisms that target agricultural production. The broad-spectrum pesticides traditionally used to combat these pests cause permanent damage to ecosystems, pose risks to human health, and lead to a critical decline in biodiversity. Precision agriculture technologies, as part of the Agriculture 4.0 revolution, offer innovative solutions to these problems. This study focuses on developing a novel artificial intelligence model capable of detecting and classifying pest insects with high accuracy, to serve as the intelligence for autonomous spraying systems. Within the scope of this research, a comprehensive and diverse dataset of over 12,000 images was meticulously compiled, featuring a total of 11 insect species, including 8 economically significant pests and 3 beneficial species for the ecosystem. To enhance the robustness and diversity of the dataset, advanced Generative Adversarial Networks (GANs) such as StyleGAN2-ADA were used alongside traditional data augmentation techniques to generate synthetic images. On this rich dataset, VGG-16 and ResNet152 V2 models, representing the transfer learning approach, were developed and comparatively analyzed against a lightweight Convolutional Neural Network (CNN) architecture custom-designed for resource-constrained edge computing devices. While the ResNet152 V2 model achieved the highest accuracy (92.4%) in laboratory tests, the custom-designed CNN model proved to be the most practical and effective solution in real-world field tests conducted with a Raspberry Pi-based prototype, delivering 82.5% accuracy, a much smaller model size (31 MB), and superior processing speed (7.5 FPS). These results strongly demonstrate that application-specific, lightweight models can exhibit more robust and efficient performance in real-world scenarios compared to complex and large transfer learning architectures. The developed system has the potential to make significant contributions to agricultural sustainability and food security by optimizing pesticide use.
Deep Learning Method for Wafer Flaw Detection in Lab-level Photolithography Subin Lee; Kyunghan Chun
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 2: June 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i2.7975

Abstract

In this paper, we propose a deep learning-based method for wafer flaw detection and classification in lab-label photolithography, known as a core step of the semiconductor manufacturing process. In photolithography, defects due to particles or process errors are critical to product yield and reliability. To detect these flaws, images were collected and efficientnet deep learning method was applied. Data augmentation and model lightweighting techniques were also applied to improve the limitations of the dataset. experimental results showed the relation between model complexity and the amount of training data. For EfficientNetB5, the massive architecture, caused the overfitting problem because of learning even noise in small datasets. But EfficientNetB0, the lightweight model, with batch normalization and early stopping techniques shows improvement of the reliability. In conclusion, this study provides practical guidelines for building and efficient flaw detection method in a data-limited research environment.
Cost-effective Flex Sensor Glove-based Bangla Sign Language (BdSL) Interpreter for Resource-limited Settings Md. Sayzar Rahman Akash; Rethwan Faiz; Mohammad Hasan Imam; G.M. Tanvirul Islam; Sheikh Tanvir Alam; Jhon Dev; Nuzat Nuary Alam
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 2: June 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i2.6933

Abstract

In Bangladesh, approximately 13 million individuals experience hearing loss, including nearly 3 million with severe to profound impairments, creating significant communication barriers for the deaf and mute community. Most existing Bangla Sign Language (BdSL) interpretation systems rely on image-based approaches. However, these systems are sensitive to environmental conditions, require substantial computational resources, and often involve high implementation costs. To address these limitations, this paper presents a low-cost wearable BdSL interpreter based on flex sensor gloves and inertial sensing. The proposed system utilizes ten flex sensors to capture finger bending and two MPU6050 gyroscope modules to measure hand orientation. Sensor data are processed using a Threshold-Based Rule Classifier (TRC) algorithm running on an Arduino Mega 2560, eliminating the need for computationally intensive machine learning or image-processing techniques. The developed prototype recognizes a total of 25 BdSL gestures, comprising 10 numerals, 10 alphabet letters, and 5 commonly used words. The system provides both text and speech outputs to facilitate communication for hearing and visually impaired users. Experimental evaluation involving five volunteers demonstrated an overall recognition accuracy of 91.2%, with recognition accuracies of 98% for numerical gestures, 80% for alphabetical gestures, and 100% for word-level gestures. Confusion matrix analysis showed macro-F1 and weighted-F1 scores of 0.9798 for numerical gestures and 0.7991 for alphabetical gestures. The complete prototype was developed at a cost of approximately 100 USD using commercially available hardware components sourced from the local market. By avoiding dependence on cameras and high-performance computing platforms, the proposed system provides robust real-time performance under diverse environmental conditions while maintaining affordability and portability. The results demonstrate the potential of low-cost wearable technology to improve accessibility and promote inclusive communication for Bengali-speaking deaf communities in resource-limited settings.
An Electronic System for Woven Fabric Quality Control Based on Image Processing Jean Carlos Carpio; Jorge Calderon; Guillermo Kemper; Luis Ayala; Christian del Carpio
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 2: June 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i2.7498

Abstract

This paper proposes the development of an electronic device that integrates image processing algorithms for quality control of woven fabrics by detecting three defects: holes, bumps, and stains. Although classic and deep learning-based image processing methodologies for detecting fabric defects exist in the literature, these proposals lack hardware integration with the software. The proposed device overcomes this limitation by integrating digital image processing algorithms implemented in Python on a single-board computer, along with control devices for the camera, lighting, and motor, to enable real-time fabric quality control. This adaptable architecture meets the needs of the textile industry and reduces the risks associated with manual inspection. The device was validated with 100 fabric samples evaluated by a textile engineering specialist, yielding a Cohen's Kappa index of 0.959 for light-colored fabrics and 0.869 for dark-colored fabrics, as well as an average inspection speed of 15.68 m/min.

Page 83 of 83 | Total Record : 825

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