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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 73 Documents
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Search results for , issue "Vol 14, No 5: October 2025" : 73 Documents clear
Fast lightweight convolutional neural network for Turkish sentiment analysis Alqaraleh, Saed; Hafiz AbdulHafiz, Abdul
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9960

Abstract

This study presents a fast, lightweight, and high-performing fast convolutional neural network (Fast CNN) model tailored for Turkish sentiment analysis (SA). The agglutinative morphology of Turkish, combined with the limited availability of high-quality linguistic resources, introduces significant challenges for conventional approaches. To address these issues, we propose a streamlined Fast CNN architecture consisting of an embedding layer, global max-pooling, dropout, and fully connected layers. Despite its simplicity, the model outperforms seven state-of-the-art convolutional neural network (CNN)-based systems across four benchmark Turkish sentiment datasets. It achieves an average area under the curve (AUC) of 0.94, representing a 6.8% improvement over the strongest baseline and a gain of over 80% relative to several deeper architectures. In addition to its superior accuracy, the model demonstrates reduced computational complexity, making it well-suited for real-world deployment in resourceconstrained environments. Potential applications include customer feedback mining and digital marketing analytics in Turkish-language domains.
The neural network adaptive behaviour model for localization and speed control in autonomous rescue mobile robot operation Hafizd Ibnu Hajar, Muhammad; Persada Nurani Hakim, Galang; Siti Salamah, Ketty; Septiyana, Diah
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9365

Abstract

In robotic operation, an autonomous operation for a mobile robot is needed to operate smoothly, hence, a control system is needed. Numerous architectures for robotics control systems have been put forth. Regretfully, creating a control system architecture is very challenging and occasionally results in inaccuracy in control. An alternative to conventional mobile robot control has emerged to address this issue: behavior-based control system architectures. This paper addresses the behavior of an autonomous mobile robot (AMR) control system in an outdoor rescue operation. The AMR behavior will be governed by the neural network methods, which are a computational intelligence to generate a dependable control algorithm. The architecture is used to coordinate behavior, especially to localize the victims, and for speed control to find the victim location with fast timing. In localization parameters to find the victim in the disaster area, this neural network adaptive model has the smallest error, which is 3.27, compared with other models such as free space model 43.46, and empirical model 4.735. While in robot speed parameter has a low error value, which is 1.47. With this small error, we can conclude that the neural network adaptive behaviour control architecture model for rescue mobile robot operation has been successfully developed.
New approach to measuring researcher expertise using cosine similarity algorithm and association rules Firdaus, Ali; Stiawan, Deris; Samsuryadi, Samsuryadi; Budiarto, Rahmat
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9506

Abstract

This study proposes a new method to assess researcher expertise using publication data. The quality of research publications is an important indicator in the ranking of universities that are undergoing diversification. Research publications have become an important indicator in the university ranking system and have a major impact on the reputation of universities as a lens for the study of expertise and prestige for human resources. Expertise is often difficult to verify objectively, as a result, many people claim to be experts or are considered experts without evidence and correct data. To ensure the expertise of researchers, it must be proven with valid data support through measurable and presentable expertise parameters. The model built uses the cosine similarity and association rule approaches. The publication variables attached to the researcher are formulated in the collaboration of the algorithm to assess the level of researcher expertise. Validation of important points of publications as parameters for measuring expertise has been identified as the main factor contributing to the measurement of researcher expertise and its impact on university reputation. The model built successfully validated researcher expertise up to 72% which is relevant to its support for university rankings up to 75%.
Digital twins and IIoT: comparison of Prometheus and InfluxDB Amirkhanov, Bauyrzhan; Ishmurzin, Timur; Kunelbayev, Murat; Amirkhanova, Gulshat; Aidynuly, Azim; Tyulepberdinova, Gulnur
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9687

Abstract

This article presents a comparative analysis of data monitoring and visualization tools—Prometheus and InfluxDB—in the context of digital twins (DTs) applied to industrial settings. DTs optimize production processes using industrial internet of things (IIoT) technologies. Mathematical models assessed the tools based on response time, resource consumption, throughput, and reliability. Prometheus is better suited for high-frequency monitoring, achieving a response time of 0.01 seconds and processing up to 10,000 metrics per second—10–15% better than InfluxDB. It consumes 1.5 times less memory (100 MB versus 150 MB), making it faster and more resource-efficient. Conversely, InfluxDB excels in long-term storage and analytics, handling up to 8,000 metrics per second with a response time of 0.09 seconds. However, it requires more resources, including higher CPU usage (20% versus 15%). Both tools integrate seamlessly with Grafana for visualization, offering flexibility for real-time monitoring and decision-making. The study provides actionable insights for selecting monitoring systems based on project-specific requirements, highlighting Prometheus’s efficiency in dynamic scenarios and InfluxDB’s strength in analytics-focused tasks.
An optimized deep learning framework based on LEE for real time student performance prediction in educational data Muniappan, Ramaraj; Devi Devarajan, Sowmya; Subbarayalu Ramamurthy, Lavanya; Balakumar, Ayshwarya; Gunaseelan, Prathap; Palanisamy, Shyamala; Selvaraj, Srividhya; Sabareeswaran, Dhendapani; Bhaarathi, Ilango
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9773

Abstract

Predicting student performance in real-time remains a critical challenge in educational data mining (EDM), especially with large, noisy, and high-dimensional datasets. This study proposes an advanced deep learning framework that integrates learning entropy estimation (LEE) with models such as support vector machines (SVM), you only look once (YOLO), recurrent convolutional neural networks (RCNN), and artificial neural networks (ANN) to enhance feature selection and classification accuracy. The framework follows a systematic pipeline involving data preprocessing, LEE-based feature extraction, and model training on a real-time academic dataset comprising student demographics, attendance, and performance metrics. Among the proposed models, the LEE-based YOLO (LBYOLO) achieved the highest testing accuracy of 93% and the fastest execution time of 1.84 seconds, while the LEE-based ANN (LBANN) demonstrated consistent performance across precision, recall, and F1-score. The results confirm the superiority of deep learning methods over traditional machine learning techniques for educational prediction tasks. This approach enables early detection of at-risk students and supports timely, data-driven educational interventions. Future work will focus on adaptive learning systems and multi-platform student behavior analysis to support personalized education strategies.
Few-shot brain tumor classification: meta- vs metric-learning comparison Akhmetzhanova, Shynar; Serek, Azamat; Kashayev, Ruslan; Kozhamuratova, Aizhan
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10706

Abstract

Medical imaging requires accurate brain tumor recognition because precise classification is essential for early diagnosis and effective treatment planning. A major challenge in medical applications is that deep learning models typically require extensive amounts of labeled data to perform well. To address this, this research evaluates three few-shot learning (FSL) approaches-prototypical networks, Siamese networks, and model-agnostic meta-learning (MAML)-for brain tumor classification using the Figshare brain tumor dataset. The results show that prototypical networks consistently outperform the other approaches, achieving 89.07% accuracy (95% CI: 88.12–89.96%), 88.73% precision, and 88.67% recall, making them the optimal solution for this task. Siamese networks achieve 83.73% accuracy (95% CI: 82.64–84.76%), while MAML demonstrates significantly reduced performance, with 43.70% accuracy (95% CI: 42.10–45.22%). This study demonstrates that FSL can be applied effectively for medical image classification, with prototypical networks achieving the best performance in brain tumor detection. The inclusion of confidence intervals further validates the robustness and reliability of the results. Future research will focus on improving feature representation and exploring hybrid approaches to better handle rare tumor classes, thereby enhancing the clinical applicability of FSL models.
Optimizing gaussian filter implementation for canny edge detection using graph-based MCM algorithms Chandaka, Lowkya; Dunna, Madhavi
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.8700

Abstract

This study presents an optimized implementation of the gaussian filter in the Canny edge detection algorithm, focusing on reducing computational complexity while balancing power, timing, and resource utilization. Traditional implementations rely on the common subexpression elimination (CSE) algorithm for multiplierless constant multiplication, which results in high logic operations and resource consumption. To address this, we explore the constant array vector multiplication (CAVM) technique with two graph-based algorithms (exact GB and approximate GB). These algorithms offer a novel graph-structured approach to constant multiplication, differing from existing methods by modeling multiple paths to achieve optimal adder reuse. The architectures were implemented using Xilinx system generator (XSG) and evaluated in Vivado 2018.1. Experimental results reveal that both exact GB and approximate GB reduce logic operations and improve timing performance compared to CSE_csd. Among them, approximate GB achieves the fastest computation and lowest LUT utilization, making it the most hardware-efficient design. However, it exhibits the highest power consumption, whereas exact GB offers the best trade-off between speed and power efficiency. This optimization framework shows potential not only in image processing but also in embedded vision systems and low-power digital signal processing (DSP) applications. These findings demonstrate that GB Algorithms can effectively optimize gaussian filter design for real-time image processing applications.
Improved load frequency control with chess algorithm-driven optimization of 3DOF-PID controller Ardhan, Kittipong; Chansom, Natpapha; Audomsi, Sitthisak; Sa-Ngiamvibool, Worawat; Obma, Jagraphon
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9871

Abstract

In contemporary hybrid power systems, persistent load fluctuations disrupt the delicate balance between electrical output and mechanical torque, thereby compromising frequency stability. Load frequency control (LFC) mechanisms are indispensable in maintaining this equilibrium, particularly in systems integrating renewable and thermal energy sources. This study introduces a three-degree-of-freedom proportional-integral-derivative (3DOF-PID) controller optimized via the novel chess optimization algorithm (COA) and evaluates its efficacy against the ant lion optimizer (ALO) and Harris Hawks optimization (HHO). Extensive MATLAB/Simulink simulations were conducted on a hydrothermal system, with performance assessed through objective functions—integral of absolute error (IAE) and integral of time-weighted absolute error (ITAE). The COA consistently yielded the lowest cumulative error values (IAE=0.1548 and ITAE=0.2965), demonstrating its superiority in steady-state performance. However, COA exhibited substantial dynamic deviations, including an overshoot of 387.79% and undershoot of 4513.8% in ∆ftie. Conversely, HHO offered a significantly enhanced transient response, achieving 0% undershoot in ∆ftie with minimal oscillatory behavior. ALO displayed moderate performance but struggled with higher undershoots and prolonged settling time. The findings underscore the criticality of algorithm selection in controller design. While COA excels in minimizing long-term errors, HHO is preferable for applications requiring heightened dynamic stability and responsiveness.
Comparative analysis of 5G network performance at Thailand's premier shopping centers Daengsi, Therdpong; Srimuk, Pachara; Puangnak, Korn; Phanthuna, Nattapong; Prajong, Amnaj; Pornpongtechavanich, Phisit
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10011

Abstract

This paper evaluates 5G network performance across three well-known shopping malls in Bangkok: Icon Siam, Siam Paragon, and CentralWorld. The study focuses on assessing key quality of service (QoS) metrics, consisting of download (DL) speed, upload (UL) speed, and latency. Measurements were taken in various zones within each mall; including high, ground, and outdoor areas through field tests using two different mobile network operators (MNO-1 and MNO-2). The findings indicate noticeable differences in performance, with Icon Siam recording the highest average DL speed of 273.6 Mbps (MNO-1) and the outdoor zone at Siam Paragon having the lowest at 11.2 Mbps (MNO-2). While MNO-1 provided more stable UL speeds, MNO-2 showed greater variability. Latency results also highlighted MNO-1’s stronger network efficiency, often staying below 20 ms, apart from a slight increase in outdoor areas. Statistical analyses, using ANOVA and t-Test, revealed significant disparities in QoS parameters depending on location and MNO, with outdoor areas often underperforming. These results underline the importance of in-building distributed antenna systems (IB-DAS) and improved infrastructure for boosting 5G performance. Furthermore, this study offers insights that can be useful to improve network quality in high-traffic locations.
MIMO-enhanced distributed spectrum sensing with diffusion based algorithms for cognitive radio systems Kandhgal Mochigar, Srikantha; Ujjini Matad, Rohitha
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9465

Abstract

Spectrum sensing (SS) is a fundamental function in cognitive radio (CR) networks, enabling efficient spectrum utilization by identifying available channels. However, existing SS methods face challenges such as low accuracy in dynamic and low signal-to-noise ratio (SNR) environments, as well as high computational complexity. To address these issues, this paper presents a distributed SS technique that combines multiple-input multiple-output (MIMO) technology with a diffusion-based (DB) cooperative algorithm. MIMO enhances spatial diversity to improve detection performance, while the DB algorithm enables efficient collaboration among secondary users, reducing both sensing time (ST) and computational time (CT). Simulations over Rayleigh (RL) and Rician (RC) fading channels evaluated metrics such as probability of detection and false alarm. Results demonstrate that the proposed MIMO-DB method outperforms existing approaches, including honey badger remora optimization (HBRO)-AlexNet, by reducing ST by 18 seconds and CT by 45 seconds at 5 dB SNR, while achieving higher detection accuracy across varying SNR levels. These findings highlight the method’s robustness and efficiency, making it a promising solution for dynamic spectrum management in 5G, internet of thing (IoT) and other next-generation wireless systems.

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