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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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Lung diseases identification using hybrid transfer learning and bidirectional long short-term memory Eka Cahyani, Denis; Tri Oktoviana, Lucky; Yasin, Mohamad; Wahyuningsih, Sapti; Dionixius, Dionixius; Maulidaningsih, Ranti; Setumin, Samsul
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.v14i2.9114

Abstract

Lung diseases rank as the third most prevalent cause of mortality globally. Accurate identification of lung disease is essential to provide appropriate medical intervention for patients. This research devised a categorization system for lung diseases using chest X-Rays (CXR). The system can identify bacterial pneumonia, viral pneumonia, COVID-19, tuberculosis, and normal CXR. The approach for detecting lung diseases utilize a combination of hybrid transfer learning and bidirectional long short-term memory. The research included convolutional neural network (CNN) models including Resnet50-BiLSTM, VGG19-BiLSTM, InceptionV3-BiLSTM, Resnet50, VGG19, and InceptionV3. The Resnet50-BiLSTM model outperforms other models in terms of accuracy and overall performance. The Resnet50-BiLSTM model achieved an accuracy of 99.87%. The models that achieve the second greatest accuracy are Resnet50, VGG19-BiLSTM, VGG19, InceptionV3-BiLSTM, and InceptionV3. The research utilizes precision, recall, and F1-Measure to demonstrate that Resnet50-BiLSTM outperforms other methods by achieving the greatest value. This research improves the performance outcomes when compared to earlier studies.
Refining CNN architecture for forest fire detection: improving accuracy through efficient hyperparameter tuning Kurniawan, Kurniawan; Perdana Windarto, Agus; Solikhun, Solikhun
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.v14i2.8805

Abstract

Forest fire detection is one of the critical challenges in disaster mitigation and environmental management. This research aims to increase the accuracy of forest fire detection through improving the convolutional neural network (CNN) architecture. The main focus of research is on efficient hyperparameter tuning, which includes selecting and optimizing key parameters in CNN architectures such as convolutional layers, kernel size, number of neurons in hidden layers, and learning algorithms. By utilizing grid search techniques and heuristic-based optimization algorithms, the resulting CNN model shows significant improvements in detection accuracy compared to previous approaches. The evaluation was carried out using a pre-processed forest fire image dataset, and the results show that architectural refinement and appropriate hyperparameter tuning can substantially improve model performance. Evaluation results comparing two models, VGG16 and the proposed method, show significant improvements over the proposed method. The proposed method shows better capabilities with an accuracy of 95.31% and a precision of 97.22%. This research contributes to developing a more reliable and efficient forest fire detection system, which is expected to be used in real applications to reduce the impact of forest fires more effectively.
Design and implementation of linear quadratic regulator control for two-wheeled self-balancing robot Gilang Buana Putra, Leonardus; Wahab, Faisal; Agustinus Tamba, Tua
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.v14i2.8689

Abstract

This research aimed to develop a control system for a self-balancing robot (SBR) based on the mathematical model of an inverted pendulum on a two-wheeled cart. The linear quadratic regulator (LQR) control was implemented to maintain the SBR’s balance under normal conditions. A linearization approach was used to convert the dynamic model into a linear form, enabling the application of LQR. Testing was conducted through simulations and a physical SBR prototype equipped with an MPU6050 sensor and NEMA 17 motor. The test results demonstrated the effectiveness of the LQR control in maintaining the SBR’s balance and its responsiveness to disturbances. Although there are differences between the simulations and physical implementation, the system successfully maintained the SBR’s balance. In conclusion, the use of the inverted pendulum mathematical model and the implementation of LQR control successfully produced a stable and effective control system for SBR balance. By testing various values of the LQR parameters, optimal robot control parameters can be obtained.
Develop a quantum key distribution application based on the BB84 protocol combined with a classical channel Nguyen, Tat-Thang; Dao, Thanh-Toan; Luc, Nhu-Quynh
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.v14i2.9051

Abstract

Amid the escalating concerns over internet security, quantum cryptography stands out as a highly promising solution for significantly enhancing the security of networking systems, emerging among them is the quantum key distribution (QKD) with the function of creating secret session keys a breeze when leveraging the intriguing properties of quantum mechanics. This study is rooted in the BB84 QKD method, where in the distribution process in the quantum realm is simulated to derive a shared key via a public channel connecting two clients with the assistance of a server, utilizing the quantum inspire (QI) platform to generate qubits within the BB84 protocol. The results, the findings regarding the performance of BB84 reveal that when the server is set up, and the key size increases to 4000 bits, the process of sending module takes 16.215 sec, the transfer module takes approximately 5.2 hours, the receive module takes 1.257 sec to finish the process for the final session key share. This indicates a noteworthy enhancement in the execution speed of QKD employing the BB84 protocol, which now holds the potential for reinforcing network security using quantum computing systems.
Fuzzy logic: a novel approach to compound noun extraction Rassam, Latifa; Zellou, Ahmed
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.v14i2.8293

Abstract

Compound noun extraction from textual documents presents a unique challenge due to the inherent complexity and variability in linguistic structures. Traditional approaches often struggle to accurately capture the nuanced semantics of compound nouns, primarily due to their rigid reliance on exact matches. In response, this research underscores the pivotal role of fuzzy logic in addressing the challenges associated with ambiguity and imprecision within compound noun extraction. Leveraging the inherent flexibility of fuzzy logic, we propose a novel approach that surpasses the limitations of traditional methods. Our method embraces the adaptability of fuzzy logic, providing a powerful and context-aware solution for compound noun extraction. Empirical evaluation demonstrates superior performance, with a macro precision of 0.572, recall of 0.607, and F-measure of 0.589, compared to traditional approaches. By incorporating fuzzy logic, our approach excels in handling variations and uncertainties present in natural language, ultimately offering a more accurate and nuanced representation of compound nouns within textual documents. This research not only advances the field of compound noun extraction but also underscores the efficacy of fuzzy logic in overcoming challenges associated with linguistic intricacies in information extraction tasks.
Automated tool for conducting emotion analysis studies in perception surveys Elías Chanchí Golondrino, Gabriel; Alejandro Ospina Alarcón, Manuel; Estella Hernandez Londoño, Claudia
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.v14i2.8238

Abstract

Considering the growing need for companies to automate the analysis of customer opinions from different digital media, this paper outlines the development of an automated tool for emotion analysis in survey responses utilizing Ekman’s six-emotion model (joy, excitement, anger, sadness, fear, and boredom). The tool processes spreadsheets containing qualitative responses and generates the percentage distribution of emotions at both individual and aggregated levels. A case study conducted with 46 systems engineering students at the University of Cartagena during the COVID-19 pandemic showed that 'anger' was the most prevalent emotion (29.3%), followed by 'excitement' (19.4%), while 'boredom' was the least frequent (2.6%). The tool demonstrated an accuracy rate of 92% in classifying emotions, compared to 90% achieved through manual coding. These results highlight the tool’s effectiveness in automating emotion analysis, providing statistical and graphical reports that aid decision-making in academic and organizational contexts.
Recent developments in vehicle routing problem under time uncertainty: a comprehensive review Yernar, Akhmetbek; Turan, Cemil
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.v14i2.8636

Abstract

This review paper examines recent advancements in vehicle routing optimization under time uncertainty, focusing on the vehicle routing problem (VRP). It sys-tematically analyzes research papers to identify strategies for optimizing routes despite temporal uncertainties, covering key areas such as optimization algo-rithms, uncertainty modeling techniques, and simulation methods. The study investigates dynamic dispatching models, reliability considerations, and multi-objective optimization approaches. By synthesizing existing literature, this pa-per presents the current state of research in vehicle routing under time uncer-tainty and suggests potential future research directions. Our findings indicate that integrating robust optimization techniques with advanced simulation meth-ods could significantly enhance decision-making processes in uncertain envi-ronments. Additionally, the paper highlights the role of machine learning and artificial intelligence in developing adaptive algorithms that respond to dynamic changes in real-time. As the need for efficient logistics solutions grows, this comprehensive review underscores the importance of addressing uncertainties in vehicle routing to improve operational efficiency, reduce costs, and enhance customer satisfaction.
Arrhythmia classification using CMF-AFF based on electrocardiogram in field programmable gate array device Revanth, Nalavade; Anto Bennet, Maria
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.v14i2.8748

Abstract

Arrhythmia classification is categorization of irregular heart rhythms depending on patterns detected in electrocardiogram (ECG) signals assist in treatment and diagnosis of cardiac conditions. ECG evaluates heart’s electrical activity to diagnose various heart conditions, but it is affected by interference or noise. ECG’s signal filtering is essential pre-processing stage that minimizes noise and highlights wave characteristics in ECG data. However, digital filters are normally constructed by multiplying coefficient and then multiplying value given as feedback which leads to more power and area consumption. To solve these issues, coefficient memory compression (CMC) technique is proposed with an adaptive FIR filter (AFF) to achieve low area and low power dissipation by compressing memory requirements in a field programmable gate array (FPGA). An adaptive FIR filter is employed to effectively minimize noise like baseline noise, muscle contraction noise, and low-frequency noise. The performance of CMC-AFF is analyzed in terms of look up table (LUT), register, digital signal processing (DSP), power, and global buffer (BufG). The proposed approach achieves a low power consumption of 0.012 W in Zed Board Zynq7000 AP system on chip (SoC) FPGA device compared to existing techniques like collateral and sequence approaches using Bartlet filter and low-power ECG processor using Bartlet filter respectively.
Effect of gamma radiation on semi-crystalline polyvinyl chloride polymer for low-voltage cable insulator Hutagaol, Antonio Gogo; Bayquni, Muhammad Ilham; Setiawan, Jan; Putranto, Dwi; Sudjadi, Usman; Sungkono, Sungkono; Kriswarini, Rosika; Masrukan, Masrukan; Yunus, Muhamad Yasin
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.v14i2.7940

Abstract

This study explores the properties of semi-crystalline polyvinyl chloride (PVC) polymer as insulation material for low-voltage (LV) cables under high gamma radiation exposure. Test samples underwent gamma radiation (60Co) at doses of 25, 50, 100, 200, 400, and 800 kGy. The evaluation encompassed surface morphology, electrical conductivity, thermal characteristics, and mechanical properties via tensile tests. Electron microscopy observation indicated surface smoothing and flattening occurred at an irradiation dose of 800 kGy. Gamma radiation with increasing doses results in similar thermogram profiles with slight differences in melting temperature and residue mass. The sample irradiated at a gamma dose of 25 kGy generates an increase in the percentage of crystallinity, indicating the occurrence of crosslinking, while other doses exhibit a decrease of crystallinity with increasing radiation dose. Tensile stress significantly dropped up to 400 kGy but increased at 800 kGy. Elongation at break (EAB) decreased with higher gamma radiation doses. Overall, materials up to 800 kGy remained non-brittle, serving as effective insulators and demonstrating thermal stability within high gamma radiation exposure conditions.
Arabic dialect classification using an adaptive deep learning model Tibi, Nejib; Anouar Ben Messaoud, Mohamed
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.v14i2.8165

Abstract

In daily life, dialect is the most widely used form of communication. Automatically identifying a dialect is a challenging task, particularly when dealing with similar dialects spoken in the same nation. In this study, we developed an automatic dialect identification of feature extraction based on the deep learning model. First, we extract the cepstral features, the fundamental frequency and glottal instances using our multi-scale product analysis (MPA) of the speech signal. These parameter measurements from the MPA of the speech signal are used as features for the designed Hamilton neural network (HNN) classifier. Our classifier considers both the external and the internal dependencies and allows one to code the dependencies by composing the multi-dimensional features as single entities as well as by determining the correlations between the elements by the recurrent operation. Experimental results show that the proposed dialect identification system achieves significant performance gains compared to current HNN-based approaches. The proposed system is rigorously designed to exploit the strong temporal and spectral relationships of speech, and its components operate independently and in parallel to accelerate processing. In addition, the experimental results indicated the robustness of our deep learning model for the identification of Arabic dialect.

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