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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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Improved non-invasive diagnosis of hepatocellular carcinoma by optimized meta classifier with hybridized features Thamby, Babitha; Jayakaran Thomson Fredrik, Edwin
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9412

Abstract

Hepatocellular carcinoma (HCC), the primary cancer of the liver, is life-threatening, with few or no symptoms, and detection in the early stage will help for successful treatment with surgery, and transplant for a better life quality. Here, we proposed two stacking classification models based on deep learning with differential hybrid feature selection for the early detection of HCC using novel non-invasive biomarker PIVKA-II. We showed how the variations in hybrid feature selection affect the performance of stacking classification and different supervised machine-learning algorithms on a metaclassifier. The base layers were support vector machine (SVM), gradient boosting (GB), and linear discriminant analysis (LDA). The meta classifier was a multilayer perceptron (MLP) with three different optimizers, stochastic gradient descent (SGD), adaptive moment estimation (ADAM), and Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS). Our first model outperformed the second with their hybrid features by improving accuracy by 1.5% and F1_score by 1% in both SGD and ADAM optimization, while MLP-LBFGS had a 1.4% increase in accuracy. The precision had hiked by 1.9%, 3.5%, and 1.7% in SGD, ADAM, and LBFGS, respectively, in Model-1. Matthew’s correlation coefficient (MCC) for MLP-SGD increased from 0.82 to 0.85, MLP-ADAM from 0.81 to 0.85, and MLP-LBFGS from 0.75 to 78 for the first model.
Integrating multi-criteria decision making and reinforcement learning for consensus protocol selection Tashatov, Nurlan; Ospanov, Ruslan; Seitkulov, Yerzhan; Satybaldina, Dina; Yergaliyeva, Banu
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9552

Abstract

The rapid progress in artificial intelligence technologies in recent years has been largely driven by advances in reinforcement learning (RL). RL methods have proven to be highly effective in solving many practical problems. Distributed ledger technologies are finding wide application in the internet of things (IoTs), providing new approaches to solving problems of traditional IoT systems. Consensus is a fundamental component of distributed ledger technologies, responsible for ensuring data consistency between nodes, its security and accuracy. This paper is devoted to the study of the optimal choice of blockchain consensus protocol for IoT networks based on a combination of multi-criteria decision making (MCDM) and RL methods. The paper discusses the potential of merging MCDM and RL methods for selecting blockchain consensus protocols in IoT networks. It suggests a combined framework for effective protocol selection and management.
CLAHE-AlexNet optimized deep learning model for accurate detection of diabetic retinopathy G., Swetha; Gupta, Gaurav; Rane, Kantilal Pitambar; Ghag, Omkar M.; Korde, Sachin K.; Lalar, Sachin; Omarov, Batyrkhan; Raghuvanshi, Abhishek
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.7854

Abstract

Diabetic retinopathy (DR) is a disease that affects the blood vessels that are located in the retina. Loss of vision due to diabetes is a common consequence of the illness and a key factor in the progression of vision loss and blindness. Both ophthalmology and diabetes research have become more dependent on computer vision and image processing techniques in recent years. Fundus photography, also known as a fundus image, is a method that may be used to capture an image of the back of a person's eye. This article presents optimized deep learning model for diagnostic marking in retinal fundus images towards accurate detection of retinopathy. For experimental work, 500 images were selected from available open source Kaggle data set. 400 images were used to train deep learning model and remaining 100 images were used to validate the model. Images were enhanced using the contrast limited adaptive histogram equalization (CLAHE) algorithm. Pre trained convolutional neural network (CNN) models-AlexNet, VGG16, GoogleNet, and ResNet are used for classification and prediction of images. Accuracy, specificity, precision and F1-score of AlexNet is better than VGG16, ResNet-50, and GoogleNet. Sensitivity of ResNet-50 is higher than other pre trained CNN models.
Design of environmental detector system application aims to promote awareness of pollution on campus Amelia, Afritha; Roslina, Roslina; Sundawa, Bakti Viyata; Azis, Abdul; Pribadi, Banu Afwan
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.8851

Abstract

Politeknik Negeri Medan (POLMED) was involved in the UI GreenMetric world rankings. The UI GreenMetric committee assessed green campus activities and environmental sustainability. The UI GreenMetric aims to raise awareness about sustainable campus greening, and social impacts of these endeavors. Based on the concept, an environmental detection system (EDS) was developed using internet of things (IoT) technology. The EDS can detect and monitor environmental parameters remotely such as carbon dioxide (CO2), noise levels, light intensity, air temperature, relative humidity, and dust particle density in real-time via the internet. Measurements of environmental parameters were conducted at one location in POLMED. The average CO2 level was 485 ppm. The average noise level was 53.40 dB. The average light intensity was 129 lux. The average air temperature was 26.60 °C. The average of relative humidity was 63.8% RH. The average of PM2.5 dust particle densities was 23 µg/m3. The average of PM10 dust particle densities was 29 µg/m3. Based on these results, the air quality has begun to be polluted because this value is already above the threshold clean quality air set by the Government of the Republic of Indonesia (310–330 ppm).
Interference management based on clustering in RIS-aided ultra dense network under multicell scenario Susanto, Misfa; Gumilang, Alisha Gita; Fitriawan, Helmy; Aziz, Azrina Abd
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9495

Abstract

Ultra dense network (UDN) and reconfigurable intelligent surface (RIS) are two latest technologies in encountering the increasing demands for network capacity and quality of service in wireless cellular networks. UDN is created by densely deploying femtocells in macrocells area. It causes complex interferences because distances among femtocells are likely very close. RIS provides solution in regulating the reflection of the signal emitted from the transmitter to the receiver to resolve the obstacles. However, RIS reflects the interference signals as well causing more complex interference problems. This paper proposes a solution using clustering method as interference management in RIS-aided UDN network. By clustering method, nearby femtocells are grouped and allocated different frequency channels among femtocells in a cluster. The performance of two systems–the baseline system and the one employing a clustering method–is evaluated based on signal to interference plus noise ratio (SINR), throughput, and bit error rate (BER). Simulation results indicate that SINR and throughput improved by 1.57% and 1.73%, respectively. Meanwhile, the BER for the baseline system is 5.78×10-8 and decreased when applying the proposed method system with a value of 2.26×10-8. The proposed clustering method is promising to confront the interference problems.
Anomaly detection in quadcopter flight: harnessing frequency domain analysis and barnacle mating optimization Sharif Zakaria, Mohd; Fadhil Abas, Mohammad; Mohd Saad, Norhafidzah; Herwan Sulaiman, Mohd; Pebrianti, Dwi
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9224

Abstract

Ensuring the safety and efficiency of unmanned aerial vehicles (UAVs) requires effective fault detection and identification (FDI). Traditional multi-stage FDI methods, particularly those using residual detection layers, increase complexity and computational cost, limiting real-time applications. This study proposes a single-stage anomaly detection framework integrating barnacle mating optimization (BMO) with discrete cosine transform (DCT) for UAV fault detection. While prior research explored model-based and data-driven FDI, bio-inspired optimization techniques remain underexplored in frequency-domain analysis. This study develops a BMO-based fitness function analyzing 3rd, 5th, and 7th harmonic peaks to detect UAV anomalies. Software-in-the-Loop (SITL) simulations validate the method, achieving a 5-second optimal frame size, mean absolute percentage error (MAPE) of 0.05, and root mean square error (RMSE) of 195.52. The findings confirm that a single-stage detection framework via optimization method and frequency domain analysis is possible, making it viable for real-time UAV applications. This study bridges the gap in bio-inspired UAV fault detection, paving the way for safer and more efficient UAV operations.
A smart ontology based model to optimize crop decision support Mancy, Hend; Elkhateeb, Amira; A. Ali, Hoda; Abdelraouf ElDahshan, Kamal
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9446

Abstract

Effective crop recommendation systems are crucial for modern agriculture, yet existing models often struggle to adapt to dynamic environmental conditions and incorporate expert knowledge. This paper proposed a novel model that fuses decision tree (DT) algorithms with ontologies, combining robust data analysis with semantic knowledge representation. DT provide transparent, adaptable decision rules that respond to changing environmental factors, while ontologies structure domain expertise to enable deeper reasoning and improve accuracy. This integrated approach achieved a remarkable 99.77% accuracy on an Indian crop recommendation dataset, significantly outperforming previous methods. By merging the strengths of DT and ontologies, this model offers a powerful, adaptable tool for informed decision-making, supporting farmers in today's complex agricultural landscape.
Unsupervised outlier detection in high-dimensional text data: a comparative analysis Sidek, Zuleaizal; Ahmad, Sharifah Sakinah Syed; Teo, Noor Hasimah Ibrahim
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9573

Abstract

Outlier detection in user reviews is a critical task for identifying anomalous and potentially valuable insights within large datasets. This study presents a comparative analysis of three different algorithms for outlier detection in user reviews: isolation forest, local outlier factor (LOF), and latent dirichlet allocation (LDA). The performance of each algorithm was evaluated using accuracy and silhouette score for outlier detection and clustering quality. LDA performed best with 0.98 accuracy and a silhouette score of 0.13. Isolation forest followed with 0.90 accuracy and a score of 0.11. LOF had lower results with 0.42 accuracy and a score of -0.05 due to its sensitivity to neighbors. The study contributes by systematically exploring the impact of parameter variations on algorithm performance, providing valuable insights for high-dimensional text data analysis. Despite the promising results, limitations include the dependence on preprocessing and specific parameter settings. Future work will explore hybrid approaches and broader datasets to enhance scalability and adaptability.
Wireless charging and monitoring system utilizing internet of things technology for electric vehicle application A/L Kalaihrasan, Prabakaran; Zainal, Nurfarina; Ngajikin, Nor Hafizah; Sapuan, Syarfa’ Zahirah; Jubadi, Warsuzarina Mat; Lee, Hing Wah
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.8630

Abstract

Internal combustion engine (ICE) vehicles are major contributors to climate change and pollution, driving the transition to electric vehicles (EVs) as a cleaner alternative. However, EVs encounter challenges with charging infrastructure, notably the need for physical cables and issues with alignment for efficient charging. To address these problems, a wireless EV charging system has been developed using internet of things (IoT) technology for real-time monitoring and control. This system incorporates ESP32 and ESP8266 microcontrollers, infrared sensors, inductive coils, an OLED display, an ESP32-CAM module, relay modules, an AC to DC converter, a TP4056 charging module, a DC voltage sensor, and lithium-ion batteries. It employs a 20-turn coil for inductive coupled wireless power transfer (WPT), enabling the full charging of two lithium-ion batteries within 60 minutes. The system can detect an EV’s presence, display battery status on an OLED screen, and provide real-time images of the vehicle’s position through the SWEVCS mobile app. Infrared sensors ensure proper and precise alignment for effective charging. This advanced wireless charging solution enhances EV charging efficiency and convenience while supporting a more sustainable energy approach.
Combination of item response theory and k-means for adaptive assessment Utomo, Wargijono; Kamdi, Waras; Sutadji, Eddy; Agus Sudjimat, Dwi
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.8962

Abstract

This study focuses on developing an adaptive assessment system for basic programming courses using a combination of item response theory (IRT) and the K-mean. The main objective is to enhance the precision of assessments by adapting the difficulty of questions to students' cognitive levels while grouping them based on both cognitive and affective characteristics. The key contribution is the creation of a more personalized assessment framework, addressing the shortcomings of traditional assessments, which often fail to accommodate varying student abilities. Methodologically, the study employs IRT to dynamically assess students' abilities, and students are categorized into different groups based on their answer patterns using K-means. The research design involves a student motivation survey and a programming skills test. Data is collected through the Google Quiz platform and analyzed using R Studio Software to apply the algorithms. The results demonstrate that combining IRT and K-means successfully adjusts the difficulty of questions and more accurately clusters students, providing more relevant feedback. In conclusion, this method enhances adaptive assessments' effectiveness and fosters personalized learning experiences. The findings have implications for broader application in courses with diverse student competencies.

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