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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 3: June 2025" : 75 Documents clear
Comparative analysis of PoS and PoA consensus in Ethereum environment for blockchain based academic transcript systems Wicaksono, Palguno; Hatta, Puspanda; Aristyagama, Yusfia Hafid
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9219

Abstract

Many educational institutions worldwide now use blockchain to verify electronic document, often relying on Ethereum 1.0, which uses proof of work (PoW) or proof of authority (PoA). However, Ethereum 2.0, launched in 2022 by Ethereum Foundation operates on proof of stake (PoS). This study provides comparative analysis of PoS and PoA consensus in Ethereum environment specifically focusing on performance and scalability in the context of academic transcript databases. To demonstrate this, a student academic reputation information system was developed using two different blockchain technologies: Ethereum 1.0 with PoA and Ethereum 2.0 with PoS. This setup was used to obtain comparative analysis data for the two blockchain systems by measuring the throughput and latency. We observed how these platforms responded to an increasing number and frequency of transactions with Hyperledger Caliper. Results indicates that in performance testing, both consensus mechanisms exhibited. Scalability tests revealed that both consensus mechanisms experienced increased latency with higher loads. However, PoA system was superior in average throughput and latency than PoS system except in high transaction of data addition. The experiment result show that PoA system better than PoS system in context of academic transcript databases, making it more suitable to be implemented on that context.
Breast cancer detection and classification using deep learning techniques based on ultrasound image Mohammed Khalaf, Abdulqader; Abdel Razek, Mohammed; El-Dosuky, Mohamed; Sobhi, Ahmed
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8397

Abstract

Breast cancer ranks as the most prevalent form of cancer diagnosed in women. Diagnosis faces several challenges, such as changes in the size, shape, and appearance of the breast, dense breast tissue, and lumps or thickening, especially if present in only one breast. The major challenge in the deep learning (DL) diagnosis of breast cancer is its non-uniform shape, size, and position, particularly with malignant tumors. Researchers strive through computer-aided diagnosis (CAD) systems and other methods to assist in detecting and classifying tumor types. This work proposes a DL system for analyzing medical images that improves the accuracy of breast cancer detection and classification from ultrasound (US) images. It reaches an accuracy of 99.29%, exceeding previous work. First, image processing is applied to enhance the quality of input images. Second, image segmentation is performed using the U-Net architecture. Third, many features are extracted using Mobilenet. Finally, classification is performed using visual geometry group 16 (VGG16). The accuracy of detection and classification using the proposed system was evaluated.
Determination analysis of main dimensions of induction motors for railway propulsion system Kamar, Syamsul; Lestari, Meiyanne; Luthfiyah, Hilda; Adam Qowiy, Okghi; Syamsuddin Hasrito, Eko; Hidayat, Sofwan
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8554

Abstract

Induction motors are used in industrial production processes. As for its use as a traction motor, it requires special design and manufacture. The type of induction motor that is widely chosen as a traction motor for railways is a squirrel-cage three-phase induction motor. The main consideration for the selection or design of an induction motor as a railway traction motor is the torque requirement to drive the train. Other parameters that are considered in the selection of an induction motor as a traction motor include available spaces for installation. This research is using a three-phase, 2,300 VAC, 480 kW, and 50 Hz induction motor. By using the application program for determining the parameters of the induction motor, it shows that the motor produces a moderate output coefficient (between maximum and minimum) and produces a torque greater than induction motor torque in general. As a result of the analysis, this induction motor is suitable to be used as a motor for the railway, where greater torque is required.
Enhancing 3D building visualization and real-time monitoring in construction through IFC and IoT integration Surya Kumara, I Made; Made Ngurah Desnanjaya, I Gusti; Nataraj, Kannan
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9263

Abstract

The integration of industry foundation classes (IFC) and internet of thing (IoT) addresses a key challenge in construction: real-time data visualization on specific building storeys. Traditional methods often struggle with data integration and timely monitoring. This study introduces a web-based platform that combines three-dimensional (3D) technology, IFC models, and IoT sensors to enhance visualization and monitoring in construction projects. Unlike prior approaches that focus on static visualization or lack real-time IoT integration, this platform delivers dynamic, storey -specific updates, enabling real-time monitoring of critical building parameters. A case study showed that file size significantly impacted loading speed, ranging from 0.17 kB/ms (97.3 kB model in 572 ms) to 11.72 kB/ms (7.2 MB model in 629 ms). Despite a slight drop in frame rate from 60 to 55 frames per second (FPS), the system maintained smooth user interactions. Memory usage increased from 180 MB to 314 MB to handle complex 3D models and IoT data in real time. These findings demonstrate that integrating IFC with IoT enhances data visualization, providing more efficient decision-making tools for construction stakeholders and improving on-site coordination and resource management.
Advanced drug recommendation using long short-term memory and type-2 fuzzy logic integration Fairuzabadi, Muhammad; Rianto, Rianto; Juang Bertorio, Margala
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9180

Abstract

This research on hybrid models for drug recommendation systems proposes long short-term memory (LSTM) and type-2 fuzzy logic (T2FL) to make its recommendations more accurate and reliable. The model leverages LSTM's ability to capture temporal patterns in medical data while addressing the inherent uncertainty through T2FL. Evaluation metrics such as mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R²), accuracy, precision, recall, F1-Score, and area under the curve-receiver operating characteristic (AUC-ROC) demonstrate that the proposed model significantly outperforms traditional models like LSTM without fuzzy, linear regression, and random forest. Integrating these two methods results in more accurate and consistent predictions, making the model highly effective in handling complex and uncertain data. Practical implications include the potential for improving personalized treatment plans and patient outcomes in clinical settings. Future research directions involve applying this hybrid approach to larger, more diverse datasets and exploring additional hybrid methods that enhance prediction accuracy and model robustness. The findings suggest that the LSTM+T2FL model is a promising tool for advancing drug recommendation systems in the medical field.
Improving complex shear modulus imaging quality through enhanced frequency combination techniques Nguyen, Cuong-Thai; Thi Thu Ha, Pham; Duy Phong, Pham; Hai Luong, Quang; Bo Quoc, Bao; Tran, Duc-Tan
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9033

Abstract

This study aims to improve the accuracy of complex shear modulus imaging (CSMI), a technique used to assess the elasticity and viscosity of soft tissues, essential for analyzing tissue structure and detecting tumors. CSMI methods are primarily divided into quasi-static and dynamic approaches, with the dynamic method estimating the complex shear modulus (CSM) by combining particle velocity measurements with force excitation. However, CSM estimation is vulnerable to errors from noise and the estimation method itself. To address noise, various filtering techniques are commonly applied. Additionally, errors from the estimation process can be minimized using approaches like frequency combination methods. In this research, we introduce an enhanced frequency combination method that substantially increases the accuracy of CSM parameter estimation, leading to higherquality CSMI outcomes. The proposed method achieves the lowest estimation error and the highest Q-index value compared to previous works. The proposed approach offers a valuable advancement in soft tissue imaging, supporting more reliable and precise diagnostic capabilities.
Hybrid image encryption using quantum bit-plane scrambling and discrete wavelet transform Rachmawanto, Eko Hari; Susanto, Ajib; Hermanto, Didik; Sari, Christy Atika; Setiarso, Ichwan; Sarker, Md Kamruzzaman
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8887

Abstract

Digital image security is increasingly vulnerable to sophisticated attacks, underscoring the urgent need for robust encryption techniques. Traditional encryption methods often fall short in defending against advanced threats, highlighting the importance of innovative solutions to protect digital images. This study tackles these challenges by incorporating quantum computing into image encryption, employing techniques such as bit-plane scrambling, pixel permutation, and bit permutation. These strategies enhance security by introducing complex, non-linear transformations that make decryption attempts significantly more difficult without the correct cryptographic keys. A key configuration based on r=44, μ=2024 is employed to achieve this. The integration of quantum bit-plane scrambling and quantum pixel permutation results in a highly secure encryption method. Experimental results show substantial improvements in entropy levels, along with strong unified average changing intensity (UACI) and number of pixels change rate(NPCR) values across various images. Notably, the "Peppers" image achieved the best performance, with UACI values of 33.5572 and NPCR values of 99.8301. The method proves highly effective, as repeated tests with incorrect keys failed to decrypt the plain image accurately. Future research could explore the addition of a discrete quantum wavelet transform to further enhance the security and efficiency of quantum-based image encryption methods.
Elliptic curve cryptography based light weight technique for information security Alshar’e, Marwan; Alzu’bi, Sharf; Al-Haraizah, Ahed; Alkhazaleh, Hamzah Ali; Jawarneh, Malik; Al Nasar, Mohammad Rustom
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8587

Abstract

Recent breakthroughs in cryptographic technology are being thoroughly scrutinized due to their emphasis on innovative approaches to design, implementation, and attacks. Lightweight cryptography (LWC) is a technological advancement that utilizes a cryptographic algorithm capable of being adjusted to function effectively in various constrained environments. This study provides an in-depth analysis of elliptic curve cryptography (ECC), which is a type of asymmetric cryptographic method known as LWC. This cryptographic approach operates over elliptic curves and has two applications: key exchange and digital signature authentication. Next, we will implement asymmetric cryptographic algorithms and evaluate their efficiency. Elliptic curve elgamal algorithms are implemented for encryption and decryption of data. Elliptic curve Diffie-Hellman key exchange is used for sharing keys. Experimental results have shown that ECC needs small size keys to provide similar security. ECC takes less time in key generation, encryption and decryption of plain text. Time taken by ECC to generate a 2,048 bit long key is 1,653 milliseconds in comparison to 4,258 millisecond taken by Rivest-Shamir-Adleman (RSA) technique.
A systematic literature review on the use of artificial intelligence for cybercrime rate forecasting Martin Morales Barrenechea, Manuel; Angel Cano Lengua, Miguel
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9213

Abstract

Cybercrime has a significant impact on the quality of life and economy of individuals, businesses and countries, and the speed of the increase has made it a pressing issue in today's digital age. This systematic review aims to identify the artificial intelligence models recently developed to forecast the rate of cybercrime and to help authorities and police forces define strategies in the fight against cybercrime. The PRISMA methodology was used with 229 articles retrieved from Scopus, IEEE and Web of Science, of which 30 met the eligibility criteria. The results showed that the traditional machine learning methods random forest, support vector machine (SVM) and logistic regression (LR) excel in their use to forecast cybercrimes by achieving more accurate results among the different methods tested. It was concluded that machine learning methods are, so far, effective in forecasting the rate of cybercrime, with accuracy ratios of up to 99.9%. However, the potential for future research lies in creating new forecasting models such as autoregressive integrated moving average long short term memory (ARIMA-LSTM) proposed in this study to improve the performance and accuracy of cybercrime forecasting.
Comparative performance analysis of software-defined networking vs conventional IP networks using IGP protocols Nicolas Viuche, Santiago; Paola Estupiñán Cuesta, Edith; Carlos Martínez Quintero, Juan
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9063

Abstract

The exponential growth of users in data networks presents significant challenges in terms of availability and traffic management. The advent of software-defined networking (SDN) technology offers new opportunities for enhancing performance and reducing operational costs. This article compares traditional data networks using conventional routing protocols like OSPF with SDN networks. An evaluation scenario was designed to assess the performance of conventional data networks configured with OSPF against those implemented with SDN using OpenFlow. Performance tests were conducted with various packet sizes, evaluating round-trip time (RTT) and jitter metrics using GNS3 and Mininet software to simulate conventional and SDN networks, respectively. The results demonstrated superior performance in SDN, with shorter transmission times; RTT values reached a maximum of 0.18 ms for packets ranging from 32 to 512 bytes, and jitter values remained below 1 ms. Furthermore, a routing analysis highlighted the need for specifying path redundancy in SDN environments via simulation scripts, a limitation not observed in conventional networks. This emphasizes the importance of addressing this issue when deploying SDN in production environments.

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