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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 3,126 Documents
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Delineating 12-lead ECG for automated ST-elevation and ST depression detection using deep learning Bambang Tutuko; Annisa Darmawahyuni; Alexander Edo Tondas; Muhammad Naufal Rachmatullah; Firdaus Firdaus; Ade Iriani Sapitri; Anggun Islami; Sukemi Sukemi; Muhammad Fachrurrozi; Siti Nurmaini; Rendy Isdwanta; Jordan Marcelino
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10531

Abstract

ST-elevation or ST-depression are markers of an abnormal heart condition detected through an electrocardiogram (ECG) where the tracing in the ST-segment is unusually elevated above the TP-segment (baseline). Identifying the localization of the ST-segment on an ECG is difficult because even a minor change in the ST-segment can be obscured by filtering processes. The 12-lead ECG signal is a non-invasive tool in the early detection of ST-elevation based on ST- and TP-segment, with quick and accurate interpretation. This study proposes a standard 12-lead ECG delineation model using deep learning (DL). The ECG signal has been segmented to Pstart–Pend, Pend–QRSstart, QRSstart–Rpeak, Rpeak–QRSend, QRSend-Tstart, Tstart–Tend, and Tend–Pstart. The study interpreted ST-elevation or -depression using an ECG delineation approach guided by medical rules. The findings revealed that the DL model achieved an average accuracy of 99.18%, sensitivity of 92.55%,specificity of 99.55%, precision of 92.61%, and F1-score of 92.52% in limb leads. Similarly, in chest leads, the DL model attained an accuracy of 99.16%, sensitivity of 93.10%, specificity of 99.53%, precision of 93.32%, and F1-score of 93.11%. This study also validated the DL-predicted results by a cardiologist from Mohammad Hoesin Hospital, Indonesia.
Comparative evaluation of AlexNet, SqueezeNet, VGG16, and ResNet50 for gender and hijab detection Aji Supriyanto; Theresia Dwiati Wismarini; Herny Februariyanti; Arief Jananto; Fitri Damaryanti; Hilmy Nurakmal Satria
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.9936

Abstract

This study aims to detect gender based on facial images with and without hijab features, with the expected outcome of distinguishing gender from these facial features. The method involves comparing the performance of four convolutional neural network (CNN) architectures: AlexNet, SqueezeNet, VGG16, and ResNet50. A total of 170 facial images were directly collected using smartphone cameras. The dataset consists of two classes: 68 male faces and 102 female faces, among which 78 images of females feature hijabs, while 24 do not. The validation stage with 40 images (15 males and 25 females) showed that the AlexNet architecture achieved the highest validation accuracy at 100%, followed by ResNet50 with 97.50%, VGG16 with 95%, and SqueezeNet with 92.50%. The testing stage with 40 images (20 males and 20 females, including 10 females with hijabs and 10 without) showed that ResNet50 classified 38 images correctly, achieving 95% accuracy. AlexNet classified 37 images correctly with 92.50% accuracy, SqueezeNet classified 36 images correctly with 90% accuracy, and VGG16 classified 34 images correctly with 85% accuracy. The contribution of this research shows that AlexNet achieves the highest validation accuracy, while ResNet50 provides the best accuracy in facial image detection for determining gender and hijab features.
Enhancement of methane detection using graphene-doped zinc oxide with Blynk internet of things for real-time monitoring Siti Amaniah Mohd Chachuli; Fatin Liyana Syakinah Mohd Yatim; Nur Hazahsha Shamsudin; Omer Coban
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.9049

Abstract

Methane is a potent greenhouse gas, significantly contributing to global warming gases when released into the atmosphere. Methane, a naturally occurring gas, has no harmful impacts on human life at low concentrations. As concentrations rise, symptoms like fatigue, headaches, nausea, irritability, and speech difficulties increase due to asphyxiation. Zinc oxide (ZnO) and graphene-doped ZnO gas sensors were fabricated using a screen-printing technique onto a Kapton film to compare their performance to methane gas at room temperature. A silver paste was used as the interdigitated electrode, deposited on the Kapton film using a screen-printing technique, and fired at 150 °C for 15 minutes. Next, ZnO and graphene-doped ZnO pastes were deposited onto the interdigitated electrode using a screen-printing technique and became the second layer of the gas sensor. The sensing layer was annealed at 200 °C for 60 minutes. All gas sensors responded well to methane gas at room temperature. As a comparison, the graphene-doped ZnO gas sensor responded better to 6,700 ppm of methane gas than the ZnO gas sensor at room temperature. The highest response of graphene-doped ZnO to methane gas was produced by 5 wt. % of graphene doped into ZnO with a response value of 11.5.
Performance analysis of rate-splitting multiple access in multi-RIS-assisted wireless communication Hong-Nhu Nguyen; Xuan-Tien Nguyen; Sang-Quang Nguyen
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10734

Abstract

This paper presents a novel rate-splitting multiple access (RSMA) framework with partial reconfigurable intelligent surface (PRIS) selection over Nakagami-m fading channels, a scenario that has not been comprehensively analyzed in prior works. To this end, we conduct a detailed performance analysis of a wireless communication system that incorporates multiple reconfigurable intelligent surfaces (RISs) as cooperative relays. The proposed framework integrates RSMA with a PRIS selection strategy, enabling efficient data transmission from the base station (BS) to two users with distinct channel conditions. Closed-form expressions for the outage probability and achievable throughput are derived over independent and non-identically distributed (i.n.i.d.) Nakagami-m fading channels. The analysis highlights the role of PRIS in enhancing system efficiency and ensuring fair resource allocation between users. In addition, asymptotic evaluations offer deeper insights into the system’s behavior under varying channel dynamics. Simulation results are provided to validate the theoretical findings, demonstrating a close match with the analytical expressions and confirming the robustness of the proposed approach.
Comparative analysis of unidirectional and bidirectional RNNs for ECG arrhythmia detection using augmented MIT-BIH data Sabura Banu Urundai Meeran; Nafeena Abdul Munaf
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10893

Abstract

Accurate classification of electrocardiogram (ECG) signals is essential for early arrhythmia detection. This study compares the performance of unidirectional and bidirectional recurrent neural networks (RNN), specifically gated recurrent unit (GRU)-based architectures, for classifying ECG beats as normal or arrhythmic. ECG data were sourced from the MIT-BIH Arrhythmia Database using the WFDB toolkit. Each beat was segmented into a 128-sample window centered on the R-peak and labeled into two classes. To address severe class imbalance (6,279 normal vs. 43 arrhythmic beats), data augmentation techniques—jittering and scaling—were applied, resulting in a balanced dataset. Both models were trained under identical conditions, with evaluation based on accuracy, precision, recall, F1-score, and other statistical metrics. The unidirectional RNN achieved poor recall (9.0%) despite high precision, yielding an overall accuracy of 54.0%. In contrast, the bidirectional RNN significantly outperformed, achieving 98.17% accuracy, 98.39% precision, 97.92% recall, and a 98.16% F1-score. The results demonstrate that bidirectional temporal modeling provides substantial improvements in ECG classification, especially for detecting minority class arrhythmias. This study highlights the importance of both data augmentation and model architecture in developing effective deep learning solutions for real-time ECG analysis and clinical diagnostics.
Pavement damage image classification using deep learning with inspection system: a case study in Morocco Youssef Aouni; Souad El Moudni El Alami; Mohammed Berrahal; Mohammed Boukabous; Mohammed Qachar
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10961

Abstract

Road and highway authorities rely on pavement management systems(PMS), in particular regular pavement condition inspections, to manage and preserve this infrastructural heritage. To this end, visual surveys are regularly conducted to detect and classify pavement damage, assess pavement condition, and derive performance indicators. However, manual pavement inspection can be a subjective and time-consuming process that requires a high level of skill from those responsible for inspection and monitoring. This study proposes a machine learning (ML) technique to automatically classifying digital images of national road surfaces captured by a camera mounted on a smart vehicle equipped with a multifunctional road inspection system (SMAC). The image dataset, captured on different roads in Morocco, includes five classes of pavement damage and one class of no damage. The experimental results indicate that the ResNet50 model achieves superior classification accuracy of approximately 94%. This research contributes to the automation of road monitoring processes and provides road managers with an effective tool for planning and executing maintenance operations with enhanced reliability and efficiency.
Dual axis solar tracker and monitoring system based on internet of things Yonis M. Yonis Buswig; Shanti Faridah Salleh; Al-Khalid Othman; Norhuzaimin Julai; Azuka Affam; Lennevey Kinidi; Hazrul Mohamed Basri; Kasumawati Lias
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.9698

Abstract

In this paper, the application of internet of things (IoT) technology in development of a dual-axis solar tracking system is presented. Sting capacity of piezoelectric material is applied a footstep energy generation system. Using Arduino MEGA as the main controller for the system, light-dependent resistors (LDRs) have been used for sunlight detection and maximum light intensity. Two servo motors have been employed to rotate the solar panel towards the position of the sun as detected by the LDR. Ethernet Shield is used as an intermediary between the hardware device and the IoT monitoring system through the Cayenne platform. Alert notifications are included to inform a remote user through phone or mail (or both) when a sensor has reached a certain predefined event. There is a 21.97 increased energy output buy the proposed system as compared to the single-axis solar tracker. Further test results of the manufactured prototype indicate that solar tracker data can be transmitted simply and monitored directly online, and the solar tracker is capable of receiving commands from the IoT monitoring application.
Effect of the retro-filling process on kraft paper and gasket materials in transformers Nor Hidayah Rahim; Imran Sutan Chairul; Sharin Ab Ghani; Mohd Shahril Ahmad Khiar; Arfah Syahida Mohd Nor; Farrah Salwani Abdullah
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.9691

Abstract

Mineral oil (MO) is the most widely used insulating liquid in oil-filled transformers. Numerous studies have investigated the use of alternative synthetic and ester-based insulating oils as replacements for MO. However, the effects of the retro-filling process on transformer materials remain insufficiently explored. This study examines the impact of retro-filling and thermal ageing on transformer kraft paper and gasket materials. Kraft paper strips, fluoroelastomer (FE) and nitrile butadiene rubber (NBR) gaskets, MO, synthetic oil (SO), and ester oil (EO) are used in the investigation. Initially, three sample bottles containing MO undergo thermal ageing for 200 hours at 130 °C. Subsequently, the retro-filling process is performed using MO, SO, and EO respectively. The samples are then subjected to an additional 200 hours of ageing at the same temperature. Results indicate that EO demonstrates superior performance in terms of shore hardness (+1.06%), while SO better preserves the tensile strength of kraft paper strips. These findings suggest that the retro-filling process can contribute to extending the service life of transformers.
Household automatic clothes dryer with temperature control Misbahul Jannah; Kartika Kartika; Mukhlis Mukhlis; Muthmainnah Muthmainnah; Arnawan Hasibuan; Widyana Verawaty Siregar; Rizky Almunadiansyah; Armen Abta; Asran Asran
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.9279

Abstract

Problems arising from the impact of irregular weather changes on the process of drying clothes, especially jeans, are an obstacle for housewives. Commercial clothes dryers are expensive and can cause fabric damage. Cleanliness and dryness of clothes play a vital role in everyday life. This article offers a solution in the form of an automatic clothes dryer with a temperature controller using the Arduino Uno microcontroller. This tool has a humidity sensor, LCD screen, indicator light, heater, fan, and DC motor. When the humidity is high, the tool automatically activates the heater, fan, and red indicator light. After the drying process is complete, the green indicator light turns on, indicating that the clothes are dry. This study aims to develop other similar research by designing a clothes dryer control system based on Arduino Uno. Experiments show that this dryer is more efficient, taking 80 minutes compared to 240 minutes of manual drying in the sun. The humidity setting at 20 HR is the set point to ensure an optimal drying process. The contribution of this research lies in the development of efficient clothes dryer technology that can be implemented practically.
Time-response modelling of an ice cream cone-shaped UWB antenna in the 5G spectrum for IoT applications Yoga Tri Nugraha; Adam Pangestu; Mawardi Mawardi; Muhammad Irwanto; Muzammil Jusoh
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10838

Abstract

The development of wireless communication technology, especially fifth-generation (5G) networks, requires ultra-wideband (UWB) antennas capable of supporting high-speed data transmission, low latency, and massive connectivity for internet of things (IoT) applications. This research proposes an ice cream cone shaped UWB antenna design with a semicircular patch, a conical transition structure, and a feed path, designed to produce a nearly omnidirectional radiation pattern with high efficiency. The design is modeled using CST Studio Suite for frequency domain analysis and MATLAB for time response analysis with a modulated Gaussian pulse signal input. Simulation results show that the antenna is able to maintain the input waveform with little amplitude attenuation and phase shift and maintain two dominant spectrum peaks at ±6 GHz without significant distortion. Return loss (S11) measurements show a bandwidth of 4.40 GHz(3.80–8.20 GHz) with a minimum value of –28 dB, while the voltage standing wave ratio (VSWR) is close to 1, indicating optimal impedance matching and low power reflection. The group delay is stable, and the 2D radiation pattern shows nearly omnidirectional characteristics. This antenna is deemed suitable for portable 5G IoT devices, with recommendations for physical fabrication and field testing.

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