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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
Adaptive voltage controller based on extreme learning machine for DC-DC boost converter Setiadi, Herlambang; Darmansyah, Darmansyah; Uji Krismanto, Awan; Yusuf Abdillah, Sulthon
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.v14i5.9647

Abstract

This study presents an adaptive voltage controller for a DC-DC boost converter using the extreme learning machine (ELM) algorithm to address the limitations of conventional control techniques under varying load and reference voltage conditions. The ELM is implemented to predict the optimal parameters of a PI controller (Kp and Ki), enabling real-time adaptability of the system. Simulation results in MATLAB/Simulink demonstrate that the proposed ELM-based proportional-integral controller (PI-ELM) outperforms both traditional PI controllers and those optimized using metaheuristic algorithms. Specifically, the controller achieved a maximum absolute error of only 0.0185 for Kp and 0.0294 for Ki across a range of operating conditions, with corresponding mean squared errors (MSE) of 0.01861 and 0.02798, respectively. These findings confirm the effectiveness of the ELM in enhancing the dynamic response and robustness of boost converter voltage regulation systems.
Hybrid 3D CNN–transformer model for early brain tumor detection with multi-modal magnetic resonance imaging Sharma, Vivek Kumar; Ameta, Gaurav Kumar
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.v14i5.11082

Abstract

Accurate and early diagnosis of brain tumors using multi-modal magnetic resonance imaging (MRI) remains a critical challenge due to tumor heterogeneity and complex spatial representation. This study proposes a novel hybrid deep learning framework that integrates a 3D convolutional neural network (3D CNN) with swin transformer blocks and an attention-based feature fusion module (ABFFM). The model leverages multi-modal MRI inputs—T1, T1Gd, T2, and fluid-attenuated inversion recovery (FLAIR)—and features a dual-branch classification head for binary tumor detection and multi-label tumor sub-region classification: enhancing tumor (ET), tumor core (TC), and whole tumor (WT). Experiments conducted on the BraTS2023-GLI dataset demonstrate that the proposed model achieves a superior classification accuracy of 96.51%, with precision of 97.98%, recall of 97.04%, and F1-score of 97.61%, outperforming state-of-the-art methods. Furthermore, intrinsic attention weights offer interpretability by highlighting modality-specific contributions. The proposed model establishes a clinically promising approach for brain tumor analysis, with strong implications for early diagnosis and treatment planning.
CODE NET: COVID-19 segmentation and detection via deep learning based networks Amina, Fareesa; Vankdoth, Krishnanaik
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.v14i5.8951

Abstract

Humans with COVID-19 have an infectious condition that affects the respiratory system. In addition to more serious conditions, headaches may be fatal for those who have the disease. Our difficulty with COVID-19 detection stems from the unreliability of computed tomography (CT) and magnetic resonance imaging (MRI) scans in identifying lung abnormalities. COVID-19 detection is a time-consuming process. In this research, a novel CODE NET model is proposed for the detection of COVID-19 virus from the gathered lung chest X-ray (CXR) images. The images are pre-processed utilizing an adaptive trilateral filter to improve the quality of the images. A reverse edge attention network (RE-Net) uses enhanced images to segment the CXR images for accurate virus detection. The segmented images are fed into a Link Net to extract relevant features and classify the COVID-19 cases. The classified cases are fed into the Grad-CAM model to generate heat maps for accurately detecting the virus. According to the result, the proposed model attains 99.75% of accuracy rate for the COVID-19 detection. The proposed CODE NET enhances the overall accuracy by 1.78%, 1.51%, and 2.20% over combined domain features-random forest (CDF-RF), Bayes-SqueezeNet, and bidirectional long short-term memory (Bi-LSTM) respectively.
The utilization of the Taguchi method on microring resonator design parameters to enhance the value of the quality factor Aminudin, Ahmad; Hasanah, Lilik; Setyo Nugroho, Harbi; Wulandari, Chandra; Mulyanti, Budi; Eka Pawinanto, Roer; Rifqi Md Zain, Ahmad; Sugandi, Gandi; Hamidah, Ida; Indrasari, Widyaningrum; Yunas, Jumril
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.v14i5.9780

Abstract

This study uses the Taguchi method to optimize the quality factor (Q-factor) of microring resonators (MRRs) for sensor applications. The MRRs are compact optical components widely used in biosensors and environmental monitoring due to their sensitivity to refractive index changes. The Q-factor, a key performance metric for MRRs, is significantly influenced by structural parameters such as ring radius (R), gap (g), waveguide width (W), and waveguide height (h). We employed a finite difference time domain (FDTD) simulation to model light propagation within the MRR and compute the corresponding Q-factor to identify the optimal combination of these parameters. An L9 orthogonal array (OA) is used in the Taguchi method to analyze each factor's influence with three levels systematically. The optimization resulted in a Q-factor of 6208.44, significantly higher than the baseline value, indicating a substantial improvement. Compared to previous works, this research highlights the advantages of combining FDTD-based electromagnetic modeling with statistical optimization, offering a structured yet efficient approach to MRR design. The proposed method enhances Q-factor performance and provides scalability for practical applications in biomedical and environmental sensing. These findings underscore the utility of Taguchi-based design in advancing the field of photonic sensor optimization.
Cross-cultural prediction of marital satisfaction using machine learning algorithms and generic needs Sponge, Khye; Ng, Kok-Why; Ting, Choo-Yee; Chai, Ian
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.v14i5.9055

Abstract

Marital satisfaction is crucial for individual well-being and family stability. Prior research has predominantly focused on Western contexts using traditional statistical models, limiting the generalizability of findings across cultures. This study addresses a significant gap by employing machine learning algorithms Naive Bayes, support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) on a diverse dataset comprising responses from 7,178 participants across 33 countries. Our methodology includes a robust data preprocessing pipeline, feature selection, and algorithm evaluation, emphasizing their practical application in relationship interventions. Using predictors derived from Maslow's generic needs, including love, respect, and pride in one's spouse, we demonstrate that these factors are significant cross-cultural predictors of marital satisfaction. Our results show that pride in spouse, love, and respect for spouse are the most significant predictors of marital satisfaction across cultures. This demonstrates the effectiveness of machine learning in capturing complex relationships, offering more accurate predictions than traditional methods. These findings suggest that fostering love, respect, and sacrifice in early relationships can significantly enhance marital satisfaction across diverse cultural contexts.
Handling partial occlusions in facial expression recognition with variational autoencoder Kemmou, Abdelaali; El Makrani, Adil; El Azami, Ikram; Hafid Aabidi, Moulay
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.v14i5.9690

Abstract

Facial expression recognition (FER) is essential in various domains such as healthcare, road safety, and marketing, where real-time emotional feedback is crucial. Despite advancements in controlled settings such as well-lit, frontal, and unobstructed conditions, FER still faces significant challenges in natural, unconstrained environments. One of the most difficult issues is the presence of occlusions, which obscure key facial features. To overcome this, multiple strategies have been proposed, generally falling into two categories: those focused on analyzing visible facial regions and those aimed at reconstructing hidden facial features. In this study, we present a variational autoencoder (VAE)-based solution designed to reconstruct facial features obscured by occlusions. Experimental results show our VAE model optimized with the structural similarity index measure (SSIM) cost function achieves superior performance, with recognition rates of 91.2% for eye occlusions and 89.7% for mouth occlusions. The SSIM-optimized VAE effectively reconstructs occlude facial features while preserving structural details, demonstrating significant improvements over conventional approaches. This VAE-based solution proves particularly robust for real-world scenarios involving common facial obstructions like masks or sunglasses, making it valuable for applications in healthcare monitoring, driver safety systems, and human-computer interaction.
Integration of deep learning algorithms for real-time vehicle accident detection from surveillance videos Mota, Riya; Wankhade, Renuka; Rahul Shinde, Gitanjali; Rajendra Patil, Rutuja; Bobhate, Grishma; Kaur, Gagandeep
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.v14i5.9587

Abstract

Major road accidents have increased due to the rapid rise of vehicles on the roads due to affordability and accessibility. While minor accidents can be resolved without the need for escorting to hospitals, significant accidents that involve the deployment of airbags necessitate the immediate attention of authorities. Thus, subsequent action of first aid and proper communication to concerned medical personnel can avoid most fatalities from accidents. The system involves the automatic detection of traffic accidents from videos extracted by closed-circuit television (CCTV) surveillance. In case of an accident, the system will detect and information about the accident will be instantly relayed to the nearest medical center. We have implemented different machine learning models such as Resnet-18, VGG-16, LeNet, and Inception V1 to ensure the accuracy of accident detection. From comparing all these models, the convolutional neural network (CNN) model shows the highest accuracy of 98%. The quick response will be an important step toward a safer and more secure transportation landscape.
Enhancing data integrity in internet of things-based healthcare applications: a visualization approach for duplicate detection Noor Basirah Md Isa, Siti; A. Emran, Nurul; Harum, Norharyati; Machap, Logenthiran; Nordin, Azlin
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.v14i5.10063

Abstract

This study addresses the critical issue of data duplication in healthcare-related internet of things (IoT) datasets, which can compromise the reliability of analyses and patient outcomes. A Python-based visualization framework using Pandas and Matplotlib was developed to detect and represent duplicate records. The methodology was applied to six cancer-related datasets sourced from Kaggle, ranging from 300 to 55,000 records, encompassing numerical, textual, and categorical data types. The visualization technique provided clear insights into duplication patterns, identifying specific counts such as 7 duplicates in the wearable device dataset, 19 in the thyroid recurrence dataset, and 534 in the synthetic healthcare electronic health record (EHR) dataset. Compared to traditional detection methods, the visualization tool facilitated faster and more intuitive initial data assessment, demonstrating its effectiveness for rapid quality checks in healthcare datasets. However, scalability limitations were observed in larger datasets, where visual clarity declined. These findings highlight the value of visualization as a preliminary data quality assessment tool and suggest future integration with advanced detection algorithms to enhance robustness and scalability.
Feature separation of music across diverse dataset: a comparative perspective Shunmugalingam Parvathi, Sakthidevi; Chandrasekar, Divya
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.v14i5.9962

Abstract

In music, feature separation is the process of separating distinguishable auditory characteristics, such as pitch, timbre, rhythm, and harmonic content, from a complicated, mixed signal. Virtual reality (VR), gaming, music transcription, karaoke systems, audio restoration, music information retrieval (MIR), music education, and audio forensics, are just a few of the areas where the topic has attracted a lot of attention. Feature extraction is crucial in music separation as it identifies and isolates sound elements, improving accuracy, and reducing noise. It simplifies raw audio into meaningful data for efficient processing and effective model learning. Without it, clean separation of audio components is very difficult. In this research, extracting features from mixed audio sources enables clean and accurate isolation of musical elements, enhancing quality, supporting precise evaluations, and boosting neural network performance across varied datasets including DSD100, MUSDB, and MUSDB18-HQ, which collectively afford rich musical content for making evaluations and benchmarks. Evaluation metrics, such as F1-score, precision, and recall, are utilized to demonstrate the performance data of the extracted features. The MUSDB18-HQ dataset yielded an overall increase of 17.86% in the F1-score metrics with significant increases in drums (+25.05%) and vocals (+20.04%), showing that the dataset was highly effective for feature separation.
Low-cost internet of things system for water metering in smart campus de Souza Medeiros, Átila; Delgado Gomes, Ruan; F. B. F. da Costa, Anderson
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.v14i5.9442

Abstract

Internet of things (IoT) technologies are transforming the monitoring of water distribution networks (WDN) and urban water infrastructure (UWI), as well as smart campus infrastructures, which has the same problems as an urban water network, such as leaks, inaccurate readings, and unnecessary expenses. Smart water meters (SWM) represent an economical IoT solution for remotely monitoring system parameters such as flow rate, pressure, and water quality to reduce losses. This paper introduces an IoT-based smart water metering solution employing message queuing telemetry transport (MQTT), long range (LoRa), a middleware for IoT, and low-cost sensors, implemented at the Federal Institute of Para´ıba, Brazil, as an initial effort toward establishing a smart campus. The evaluation of the IoT device showed a measurement performance index (MPI) of 97.83%, with a flow sensor error margin (FS400A) below 2% for calibrated ranges. The quality of the wireless link yielded an average RSSI of-89 dBm and a packet error rate of 0.35%. The IoT system demonstrated potential as a feasible smart campus application

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