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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 2,901 Documents
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Comparative analysis of Haar Cascade, OpenCV, and you only look once algorithms for vehicle detection Kaur, Gagandeep; Pawar, Shital; Patil, Rutuja Rajendra; Patil, Amol Vijay; Yenkikar, Anuradha V.; Bhandari, Nikita; Kadam, Kalyani Dhananjay
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10554

Abstract

Object detection is one of the substantial tasks in computer vision and has a wide range of applications ranging from autonomous driving to monitoring systems. This study presents a comparative analysis of vehicle detection approaches, contrasting traditional methods (OpenCV contour analysis and Haar Cascade) with modern deep learning-based you only look once version 8 (YOLOv8) and its variants. Vehicles were identified and localized within video frames using bounding boxes, with performance assessed through accuracy, F1-score, mean average precision (mAP), and inference speed. YOLOv8 consistently achieved superior accuracy (up to 98% in specific scenarios) and real-time processing speeds (155 FPS), confirming its suitability for safety-critical applications such as intelligent transport systems and autonomous navigation. However, its higher computational and memory demands highlight deployment trade-offs, where lighter variants like YOLOv8s remain feasible for embedded or low-power devices. In contrast, Haar Cascade and contour analysis offered faster execution and smaller memory footprints but lacked robustness under complex environmental conditions. The study also acknowledges limitations such as dataset bias, adverse weather effects, and scalability challenges, which may impact generalization in real-world deployments. By analyzing these trade-offs, the work provides essential insights to guide practitioners in selecting suitable vehicle detection solutions across diverse application environments.
Real-time vehicle detection and speed estimation system using Raspberry Pi and camera module Jyothi, B; Pabbuleti, Bhavana; Sanjeev, Gadi; Rao, Kambhampati Venkata Govardhan; Srilakshmi, S. Sai; Jee, Atul; Kumar, Malligunta Kiran; Bikku, Thulasi; Reddy, Ch. Rami
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.9931

Abstract

In the era of intelligent transportation systems, real-time vehicle detection and distance estimation play a crucial role in enhancing road safety and traffic efficiency. This study proposes a low-cost, real-time system that integrates you only look once–version 8 (YOLOv8)-based deep learning for vehicle detection with monocular vision techniques for distance estimation, implemented on a Raspberry Pi embedded platform. The objective is to provide a scalable, affordable solution for traffic monitoring and collision avoidance in resource-constrained environments. The methodology involves using a camera module connected to Raspberry Pi for live video capture, YOLOv8 for object detection, and a calibrated monocular distance estimation algorithm based on bounding box dimensions and known vehicle sizes. Experimental results show that the system achieves over 90% detection accuracy under standard lighting conditions and maintains a distance estimation error below 10% for vehicles within 15 meters. The model processes video frames in real time (~0.17 seconds per frame), proving its effectiveness for embedded deployment. In conclusion, the proposed system offers a robust, power-efficient alternative to high-cost light detection and ranging (LiDAR) or stereo vision systems. Its modular design supports future enhancements such as speed estimation or multi-camera integration, making it highly relevant for smart city applications and low-cost vehicular safety systems.
Integrating RPA, BPM, and DT in the context of Industry 4.0 and 5.0: a strategic approach for modern enterprises Bui Quang, Truong; Dang Quoc, Huu; Nguyen Thi Cam, Van; Nguyen-Duc, Anh
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10535

Abstract

This paper examines the interaction between robotic process automation (RPA), business process management (BPM), and digital transformation (DT)-three critical components in improving operational efficiency and driving business modernization. RPA automates repetitive tasks, reduces errors, accelerates processing, and optimizes resource use. When combined with artificial intelligence (AI) and machine learning (ML), it further enhances data analysis and decision-making. BPM focuses on analyzing, designing, and optimizing business processes to ensure organizational agility. DT provides a technological foundation for broader innovation in processes and structures. The paper contributes a comprehensive and updated perspective on how RPA, BPM, and DT interrelate—not only functioning independently but also reinforcing one another to create greater business value. It emphasizes that their integration is a strategic approach to improving performance, responsiveness, and continuous innovation. Importantly, the research is relevant to both Industry 4.0 and Industry 5.0. While Industry 4.0 (I4.0) prioritizes automation and data-driven systems, Industry 5.0 (I5.0) highlights human–technology collaboration for more adaptive and human-centric organizations. This study enriches theoretical insights and offers practical guidance for building effective and sustainable DT strategies.
Alzheimer's disease detection based on MR images using the quad convolutional layers CNN approach Pamungkas, Yuri; Syaifudin, Achmad; Yunanto, Wawan; Hashim, Uda
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10304

Abstract

Alzheimer’s disease is a progressive neurodegenerative disorder requiring early and accurate detection for effective intervention. Deep learning (DL) techniques, particularly convolutional neural networks (CNNs), have shown promise in medical image classification. However, conventional CNN models often suffer from high computational complexity and inefficiency in handling imbalanced datasets. This study proposes a quad convolutional layers-CNN (QCL-CNN) for Alzheimer’s disease detection using magnetic resonance images (MRI) scans from the open access series of imaging studies (OASIS) dataset, which includes four dementia stages, non-dementia, very mild dementia, mild dementia, and moderate dementia. The QCL-CNN model employs four sequential convolutional layers for enhanced multi-level feature extraction, ensuring efficient classification while minimizing computational overhead. The experimental results demonstrate that QCL-CNN outperforms traditional CNN architectures, achieving an accuracy of 99.90%, recall of 99.89%, specificity of 99.93%, and an F1-score of 99.52%. The model surpasses VGG19, Xception, ResNet50, and DenseNet201 while maintaining a significantly lower parameter count (4.2M), making it computationally efficient. These findings confirm that network optimization is more crucial than model depth, ensuring robust performance even with fewer layers. Future research should explore multi-modal imaging, class balancing techniques, and real-world clinical validation to further improve the model’s diagnostic capabilities. The QCL-CNN model offers a promising artificial intelligence (AI)-powered approach for early Alzheimer’s detection, enabling precise, and efficient medical diagnosis.
Advances in artificial intelligence-driven 3D model generation: a review of GAN and VAE methodologies Adilkhan, Shyngys; Alimanova, Madina; Shi, Lei; Soltiyeva, Aiganym
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10755

Abstract

This paper offers a comprehensive review of current developments in artificial intelligence (AI)-based 3D model creation, with an emphasis on techniques utilizing variational autoencoders (VAEs) and generative adversarial networks (GANs). 3DGAN, paired 3D model generation with GAN, conditional GAN, FaceVAE, voxel-based 3D object reconstruction, and 3D-VAE-SDFRaGAN are the six main techniques that are studied in this work. Each method is discussed, highlighting its architectural framework, data representation, and specific approach to generating 3D models. First, the paper introduces basic terms and classical 3D modeling techniques and provides a comparative analysis of them based on their workflow, purpose and field of application. In subsequent chapters, methods for generating 3D models based on the use of GANs and VAEs are reviewed, describing its methodology, experimentation technique, results, and comparison with other methods. The review outlines the strengths and limitations of each approach and their applications in object reconstruction, shape generation, and maintaining model consistency. It concludes by emphasizing how AI-driven methods can advance 3D modeling, underscoring the need for further research to enhance quality, control, and training reliability. The findings show AI’s significant impact on automating complex modeling tasks and enabling new creative opportunities in 3D content development.
IoT-based real-time monitoring of agricultural wastewater using Raspberry Pi, Node-RED, and Grafana Faizu, Nur’in Batrisyia Mohd; Roslizar, Ahmad Muzammil; Zaini, Muhammad Aizat Zaim; Idris, Fakrulradzi; Berahim, Zulkarami; Latiff, Anas Abdul
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10170

Abstract

This study introduces an internet of things-based agricultural wastewater monitoring system (IoT-AWMS) designed to enhance water management through real-time monitoring and advanced sensor integration. The system employs a Raspberry Pi for centralized control, node-RED for automation, InfluxDB for data storage, and Grafana for visualization. A key innovation is the integration of an alternative sensing approach for estimating electrical conductivity (EC), complementing conventional sensors for total dissolved solids (TDS), water temperature (DS18B20), and ambient conditions (DHT11). The system achieves over 85% accuracy in estimating EC across diverse water samples, including drinking water, agricultural runoff, and fertilizer-enriched solutions. Compared with conventional approaches, IoT-AWMS demonstrates superior accuracy, scalability, and cost-effectiveness. Its modular design supports applications in nutrient runoff detection, contamination monitoring, and optimized water resource utilization, with broader potential in precision farming and environmental monitoring. This work contributes a robust, adaptable IoT framework for sustainable agricultural water management.
Influence of installing a virtual synchronous generator control on Lombok Island power grid with high penetration of PV plants Setiadi, Herlambang; Mithulananthan, Nadarajah; Nuris Syifa, Baity; Ricky Ananda, Yoshiko; Cahya Anugrah Haebibi, Riski; Afif, Yusrizal
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.9733

Abstract

Indonesia is a country with several islands, and providing clean energy in islanded power systems connected to a single main grid would be economy challenging. On the other hand, absence of inertia, system strength, and damping value in islanded power systems due to inverter interfaced renewable energy (RE) resources can cause significant decline of power system stability. The primary concern with integrating large scale photovoltaic (PV) power plant in an islanded power system is maintaining frequency and voltage stability. This research investigates the application of virtual synchronous generator (VSG) in Lombok’s Islanded power system, considering high penetration of PV. A thorough time domain simulation is performed with a detailed modelling of power system in Lombok Island to study the dynamic voltage and frequency stability. The simulation results show that the VSG improves both frequency and voltage stability in transient and steady state stages, ensuring smoother operation and faster stabilization time. It is found that the frequency deviation can be curtail up to 0.5% and the steady state can be increased up to 0.1%.
Intelligent building automation system using ESP32, Azure and internet of things technologies Cardoza, Fernando; Samamé, Cristy; Yauri, Ricardo; Castro, Antero
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.11021

Abstract

The adoption of home automation systems in buildings faces limitations due to their cost, integration complexity, and protocol heterogeneity, which hinders the development of accessible solutions based on embedded devices to improve interaction in environments within buildings or homes. The literature review indicates that the selection of hardware and communication protocols in home automation systems considers factors such as cost, available infrastructure, and application context. In addition, approaches are identified that prioritize security, wired or wireless connectivity, and affordability. This paper presents the development of an affordable home automation system for building automation in Lima, using the ESP32 microcontroller and internet of things (IoT) technologies. The objectives focus on hardware design, implementation of control algorithms, remote monitoring interface, and validation in a simulated environment. The solution includes Wi-Fi connectivity, a cloud-based MySQL database, and a web interface. Key findings include the home automation system, integrated with Flask technology and web services, enabling monitoring and control via a responsive web interface, demonstrating its operability and ensuring lossless data transmission.
Route splitting and adaptive mutation in genetic algorithms for the capacitated vehicle routing problem Kadyrov, Shirali; Turan, Cemil
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.9204

Abstract

The capacitated vehicle routing problem (CVRP), where vehicle capacity constraints limit the load carried per route for multiple vehicles, is addressed using an optimized genetic algorithm (GA) framework. This work focuses on finding the best configuration of GA by systematically evaluating 12 distinct GA variants, differing in adaptive mutation rates and route-splitting strategies. The framework integrates adaptive mutation rates and novel route-splitting approaches—greedy, dynamic programming (DP), and heuristic—to enhance computational efficiency and solution quality. Experiments on six CVRP instances of varying complexity, encompassing differences in problem size, vehicle capacity, and geographical distribution, demonstrate the heuristic approach’s effectiveness. It achieves solutions within 2%–5% of the optimal cost of DP while being 3–4 times faster. Adaptive techniques reduce costs by up to 20% compared to standard GAs and heuristics. The framework’s scalability is evident in large-scale instances such as the 200-customer case, where the heuristic method balances cost (414.17) and computation time (0.003 seconds). The developed software is openly available at GitHub, providing a robust tool for addressing practical logistics challenges.
Brain tumor classification using PCA-NGIST features with an enhanced RELM classifier Babu, Bukkapatnam Rakesh; Rajesh, Vullanki; Rajanna, Bodapati Venkata; Ahammad, Shaik Hasane
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10742

Abstract

Brain tumours may cause severe health risks because of abnormal cell growth, which may result in organ malfunctions and death in adulthood. As precise identification of the tumour type is required for effective treatment. Magnetic resonance imaging (MRI) has recently been provided as an effective method for brain tumour diagnosis by computer-based based systems. To categorize brain tumours from MRI images, the paper offered a fusion model integrating an enhanced regularized extreme learning machine (RELM) classifier with principal component analysis (PCA) and normalized GIST (NGIST) feature extraction. While NGIST extracts strong spatial and texture features essential for modelling the tumour, PCA reduces the dimension of the input features without sacrificing significant data patterns. The improved RELM efficiently categorizes brain tumours into three categories: pituitary, meningioma, and glioma. It is optimized to improve learning capacity and generalization. The novelty of this study lies in the integration of NGIST descriptors with PCA-driven dimensionality reduction and an enhanced RELM classifier in a single lightweight framework. Unlike conventional methods that trade accuracy for computational cost, the proposed model ensures high precision and recall while remaining computationally efficient. This unique fusion demonstrates significant improvements in both diagnostic accuracy of 96% and clinical applicability, offering a balanced solution for real-time brain tumor classification.

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