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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 75 Documents
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Search results for , issue "Vol 14, No 2: April 2025" : 75 Documents clear
Multimodal deep learning from sputum image segmentation to classify Mycobacterium tuberculosis using IUATLD assessment Saurina, Nia; Chamidah, Nur; Rulaningtyas, Riries; Aryati, Aryati
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.9250

Abstract

Tuberculosis (TB) continues to be a major global health issue, especially in areas with limited resources where diagnostic tools are often insufficient. Traditional TB detection methods are slow and lack sensitivity, particularly for early-stage or low bacterial load cases. This study introduces a new multimodal deep learning model that integrates sputum image segmentation across RGB, hue, saturation, and value (HSV), and CIELAB color channels, using the YOLOv8 model for real-time detection and segmentation. The model uses the International Union Against Tuberculosis and Lung Disease (IUATLD) grading scale for accurate Mycobacterium tuberculosis (MTB) classification. Our approach shows high accuracy (92.24%) and precise forecasting (mean absolute percent error (MAPE) of 0.23%), greatly enhancing diagnostic speed and reliability. This research offers a novel method for classifying MTB using a multimodal deep learning model that integrates sputum image segmentation across RGB, HSV, and CIELAB color channels. By using the YOLOv8 model for real-time bounding box detection and segmentation, and the IUATLD grading scale for classification, our method achieves high accuracy and precision in identifying TB bacteria. Our findings indicate that this multimodal deep learning approach significantly improves diagnostic accuracy and speed, providing a reliable tool for early TB detection.
Handwritten Kaganga script classification using deep learning and image fusion Dwika Putra, Erwin; Ermatita, Ermatita; Abdiansah, Abdiansah
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8747

Abstract

Classification of traditional handwriting script and to preserve many cultures have been developed in some parts of the world, including image classification of handwriting Kaganga script. This study aims to propose a new combination model by implementing top-hat transform (THT) and contrast-limited adaptive histogram equalization (CLAHE) with discrete wavelet transform (DWT) to support the performance of the convolutional neural network (CNN) in Kaganga script classification. The top-hat transform and contrast-limited adaptive histogram equalization with discrete wavelet transform Fusion L2 convolutional neural network (DWT-THCL L2 CNN) models get the best accuracy from the CNN with L1 regularization, CNN with dropout regularization, CNN with L2 regularization and CNN with L2 regularization and CLAHE models. Based on the experimental results, the DWT-THCL L2 CNN model successfully increased training accuracy by 7.76%, validation accuracy by 5.11%, and testing accuracy by 3.73% from the CNN L1 model. The DWT-THCL L2 CNN model received a training accuracy of 99.87%, validation accuracy of 82.61%, and testing accuracy of 82.61%, while the CNN model with L1 regularization (L1 CNN) only received a training accuracy of 92.11%, validation accuracy of 77.50%, and testing accuracy of 78.88%.
Optimization of dynamic transmission network expansion planning using binary particle swarm optimization algorithm Inyanga, Faith Eseri; Muisyo, Irene N.; Kaberere, Keren K.
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8944

Abstract

Increasing power demand is usually met by the expansion of generation capacity. The transmission network should be expanded in tandem to ensure power is evacuated from generation points to the load centres. Inadequate power capacity causes congestion. Congestion results due to under-voltages and violation of transmission lines’ loading limits. Constructing additional transmission lines is required to alleviate the congestion after measures of increasing the transmission line’s transfer capability are exploited. Transmission network expansion planning (TNEP) determines the transmission lines to be added to a power system at minimal construction cost, without violating network constraints. In this research, voltage limit violations are penalized in a constrained dynamic TNEP problem for a 10-year planning horizon. The optimal location and number of new transmission lines required at minimal construction cost, and transmission losses associated with the transmission network operations are determined. Improved binary particle swarm optimization (IBPSO) algorithm is applied to optimize the dynamic transmission network expansion planning (DTNEP) results. The developed model is tested on Garver’s 6-bus system using MATLAB. The construction cost for new transmission lines is minimized, and transmission losses reduced when compared to other published works without violating voltage limits (±5%) and transmission lines’ thermal capacities. The transmission network system adequacy is improved.
Sliding mode control for speed loop combined with MTPA strategy of IPMSM applied in electric vehicles Thi Hoai Thu Anh, An; Hung Cuong, Tran; Minh Chien, Duong
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8581

Abstract

The interior permanent magnet synchronous motor (IPMSM) 's outstanding features, such as quick torque mobility capability, broad speed adjustability, robust mechanical structure, and high efficiency, make it particularly suitable for electric vehicle propulsion systems. This paper proposes a speed loop utilising the sliding mode control (SMC) with exponential reaching law and proportional-derivative term-ks, facilitating quicker transient responses and enhancing system stability. Moreover, coupling with the maximum torque per ampere strategy (MTPA) on current to improve motor torque in flux weakening region and to extend the adjustable range of motor speed for electric vehicle propulsion systems is discussed. Furthermore, with the proposed control methods and strategies, the system achieves stability despite environmental noise and uncertainties caused by uncertain parameters. Finally, simulation results conducted on MATLAB/Simulink software verify the correctness of the proposed control methods.
Driving training-based optimization technique for estimating synchronous motor excitation current Murugesan, Karthikeyan; Ramasubbu, Rengaraj
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8579

Abstract

This paper introduces an innovative application of the driving training-based optimization (DTBO) technique to optimize a multiple linear regression (MLR) model for estimating synchronous motor (SM) excitation current. Inspired by structured learning in driving training, DTBO is utilized to accurately determine regression coefficients with fast convergence. The DTBO-based MLR model is compared with other optimization techniques, such as gravitational search algorithm (GSA), artificial bee colony (ABC), genetic algorithm (GA), symbiotic organisms search (SOS), and various machine learning algorithms. Using a dataset of 557 samples (390 for training, 167 for testing), the DTBO-based model achieves the lowest objective function value, demonstrating superior performance in minimizing estimation errors. Key metrics like maximum error, error percentage, standard deviation, and root mean square error (RMSE) validate the results. The DTBO-based approach not only outperforms other methods but also provides a clear mathematical relationship between excitation current and input features, enabling easier hardware implementation and faster computation. This study establishes the DTBO-based MLR model as a robust and efficient alternative to complex machine learning algorithms for estimating SM excitation current, offering significant contributions to power systems engineering and smart grid applications.
Transformers for aerial images semantic segmentation of natural disaster-impacted areas in natural disaster assessment Wiria Nugraha, Deny; Ahmad Ilham, Amil; Achmad, Andani; Arief, Ardiaty
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8454

Abstract

Aerial image segmentation of natural disaster-impacted areas and detailed and automatic natural disaster assessment are the main focus of this study. Detecting and recognizing objects on aerial images of areas impacted by natural disasters and assessing natural disaster-impacted areas are still difficult problems. To solve these problems, this study utilizes four of the latest transformer-based semantic segmentation network models, bidirectional encoder representation from image transformers (BEIT), dense prediction transformer (DPT), OneFormer, and SegFormer, and proposes a detailed and automatic natural disaster assessment of the segmented image. The SegFormer model achieved the first-best result, and the OneFormer model achieved the second-best result. The SegFormer model outperformed OneFormer by 1.58% higher for the mean accuracy value and 4.28% for the mean intersection over union (mIoU) value. All receiver operating characteristics (ROC) curves have mean area under curve (AUC) values above 0.9, which means that the SegFormer model performs well in generating semantic segmentation images. The fuzzy c-means (FCM) clustering algorithm performed well and could automatically cluster the natural disaster assessments into four categories. This study has produced semantic segmentation of aerial images of areas impacted by natural disasters and natural disaster assessments, which can be used in natural disaster management systems.
Medication box management system with automatic dosing integrated with IoT-based Android app and Firebase Nurhakim, Raihan; Ath Thahirah Al Azhima, Silmi; Fahmi Arief Hakim, Nurul; Al Qibtiya, Mariya; Maulana, Luthfi; Rohman, Saepul
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8470

Abstract

Utilizing Firebase technology and an Android internet of things (IoT) application, the research endeavors to create a smart medicine box in order to enhance the efficacy of automated drug management. Hardware implementation, software implementation, and 3D design planning for automatic dosage adjustment are the methods utilized. The results prove that the application effectively controls the dosage, evacuation schedule, and quantity of the medication based on the user’s input. Boundary value analysis (BVA) black box testing demonstrated that every feature of the application functions as intended. Furthermore, the efficacy of drug production testing indicates that the smart medicine box exhibits a notable level of precision, albeit with a limited number of inaccuracies that could be rectifiable through additional parameter and mechanism optimizations within the drug box. Consequently, the investigation has effectively produced an automated drug management system that has the potential to enhance drug use supervision and safety, particularly for elderly services individuals residing alone.
Electromagnetic interference risk from electrostatic discharge in infant incubators Trivida, Elvina; Sudrajat, Muhammad Imam; Ardiatna, Wuwus; Prananto, Haryo Dwi; Nugroho, Hutomo Wahyu; Yoppy, Yoppy; Anam, Mohamad Khoirul; Bakti, Aditia Nur; Mandaris, Dwi; Arjadi, R Harry
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8882

Abstract

This paper proposes an improved electromagnetic compatibility (EMC) risk analysis approach for medical equipment related to the effect of electrostatic discharge (ESD). This approach not only focuses on the risk of ESD from the susceptibility aspect but also investigates its conducted electromagnetic interference (EMI) characteristics. This study combines the standardized ESD test and conducted emission (CE) measurement simultaneously, applying it to the infant incubator and analyzing the spectrum of ESD current in the phase line in the time and frequency domain. The result shows that an ESD exposure caused current spikes with an average level of 13.8 A. Moreover, it also causes a broad spectral CE noise on the phase line of the infant incubator. Furthermore, the CE noise in the low-frequency range was also detected on the phase line during ESD exposure, indicating the risk of interference with other sensitive medical equipment connected to the same power network. The approach of proposed risk analysis in this study can be used to identify the risks of EMI due to ESD events in implementing the latest IEC 60601-1-2.
Linear and nonlinear control design for a quadrotor Hadid, Samira; Boushaki Zamoum, Razika; Refis, Youcef
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8234

Abstract

In the current study, the quadrotor's nonlinear dynamic model is developed using the Newton-Euler approach. Following that, several nonlinear and linear control strategies for tracking the quadrotor's trajectory are applied. First, by employing distinct controllers for each output variable, direct application of the linear proportional integral derivative (PID) controller to the nonlinear system is realized. This system may also be linearized about an operational point to generate linear controllers, according to the linear quadratic regulator (LQR) demonstration. Nevertheless, in practice, the system dynamics may not always be accurately reflected by this linear approximation and may even be relatively wasteful. Nonlinear regulators, including the feedback linearization (FBL) controller, sliding mode controller (SMC), and modified sliding mode controller (MSMC), perform better in such situations. The trajectory tracking capabilities, dynamic performance, and potential disruption impact of both methods are evaluated and compared. The FBL with LQR was the best controller among them all. The SMC and the MSMC were also very good in tracking the trajectory.
Deflection enhancement of ferrite magnetic core-based microactuator Pawinanto, Roer Eka; Mulyanti, Budi; Fauzan, Jahril Nur; Subandi, Ayub; Hasanah, Lilik; Pangestu, M. Assadillah; Yunas, Jumril
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8448

Abstract

Microactuators play a vital role in several microelectromechanical systems (MEMS) that generate forces or deflections necessary to accomplish functions such as scanning, tuning, manipulation, or delivery. Utilizing a ferrite magnetic core has shown the potential to enhance the deflection of the microactuator. However, the previous study presented a complex fabrication method with high power consumption unsuitable for micropump application. Herewith, we report the impact of ferrite core length on the deflection generated by a microactuator with a simple fabrication method. The deflection behavior shows that the corresponding magnetic core length is inverse to the deflection improvement. The force reduction generated led by a longer magnetic core because of the farther distance to the coil. Our study can be used as a reference to support the development of micropump or active micromixer devices, which require compact devices with simple fabrication and high deflection, achieving ultra-high flow rate and high mixing index.

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