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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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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.
Multilayer crypto method using playing cards shuffling operation J. Rasras, Rashad; Rasmi Abu Sara, Mutaz; Alqadi, Ziad
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.v14i3.9183

Abstract

An efficient and highly secure method of secret message cryptography will be presented which based on the principle of playing cards shuffling. The method will be implemented in a selected number of layers, each layer will encrypt-decrypt the input message using its own private key (PK), the output of any layer can be taken as a final encrypted-decrypted message, increasing the number of layers will increase the security level of the massage, making the hacking attacks impossible. In the encryption function a key generation and a message blocks shuffling will be executed, while in the decryption function the key generation and the message blocks reverse shuffling will be executed. The PK used in this method will be complicated and it will contain for each layer 2 chaotic parameters (r and x) and the block size (BS), utilizing these parameters, a chaotic logistic map model is run to produce a chaotic key, which is sorted to produce the layer's index key. Applying 4 layers the length of confidential key will be 768 bits, this length will be able to generate a large key space which is robust to hacking attempts. The speed parameters and throughput of the proposed will be calculated and compared with those of other methods.
Enhancing manufacturing efficiency: leveraging CRM data with Lean-based DL approach for early failure detection Kalluri, Venkata Saiteja; Malineni, Sai Chakravarthy; Seenivasan, Manjula; Sakkarai, Jeevitha; Kumar, Deepak; Ananthan, Bhuvanesh
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.v14i3.8757

Abstract

In the pursuit of enhancing manufacturing competitiveness in India, companies are exploring innovative strategies to streamline operations and ensure product quality. Embracing Lean principles has become a focal point for many, aiming to optimize profitability while minimizing waste. As part of this endeavour, researchers have introduced various methodologies grounded in Lean principles to track and mitigate operational inefficiencies. This paper introduces a novel approach leveraging deep learning (DL) techniques to detect early failures in manufacturing systems. Initially, realtime data is collected and subjected to a normalization process, employing the weighted adaptive min-max normalization (WAdapt-MMN) technique to enhance data relevance and facilitate the training process. Subsequently, the paper proposes the utilization of a triple streamed attentive recalling recurrent neural network (TSAtt-RRNN) model to effectively identify Leanbased manufacturing failures. Through empirical evaluation, the proposed approach achieves promising results, with an accuracy of 99.23%, precision of 98.79%, recall of 98.92%, and F-measure of 99.2% in detecting early failures. This research underscores the potential of integrating DL methodologies with customer relationship management (CRM) data to bolster early failure detection capabilities in manufacturing, thereby fostering operational efficiency and competitive advantage.
A novel technical analysis and survey on disaster robots for flood search and rescue operations Duvvuru, Rajesh; Jagadeeswara Rao, Peddada; Narasimha Rao, Gudikandhula; Rayachoti, Eswaraiah; Boyidi, Suribabu; Prathyusha, Kodamala
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.v14i3.7364

Abstract

The advance in human-robot interaction brings out novel applications of the disaster rescue operations. Especially, the concept of search and rescue (SAR) assisted robot operations plays an extensive role in the natural hazards, such as earthquake and wild fires. Particularly, the SAR operations in the water-based drowning due to floods and boat capsize disaster are expensive and not fast. This paper presents a survey on various SAR based remotely operated vehicle’s (ROV) related to airborne, under and surface of the water, such as unmanned marine vehicles (UMV) and unmanned aerial vehicles (UAV). In addition, the performance analysis of each UMV such as EyeROV TUNA, Saif Seas, iBubble, DTG3, Trident, Fathom One and SEAOTTER-2, is listed which helps to select the right UMV for the rescue operation at different water depths. Also discussed various SAR based UAVs like DJI Phantom-MAVIC 2, YUNEEC-H520 Hexacopter, Microdrones MD4-1000, DSLRProsMatrice 210 RTK V2 and AltiGator’sXena Drone for the flood and boat capsize operations. However, the usage of Syma X8 Pro UAV for the flood operations are worthy than Sea King SAR Chopper, which is a cost-effective operation.
A systematic literature review to address overlapping laws in Indonesia Akhyar, Amany; Saptawati, Gusti Ayu Putri
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.v14i3.8407

Abstract

The vast number of laws often result in legal uncertainty due to overlapping, conflicting, and inconsistent regulations. Identifying and resolving these overlaps is essential for ensuring legal clarity and coherence. This systematic literature review (SLR) explores technologies that have the potential to address the issue of overlapping laws in Indonesia. This study reviews numerous works on knowledge graphs (KGs) and graph mining, focusing on their potential to automate the detection of overlapping laws, thereby streamlining the process of legal harmonization. The review identifies several key research opportunities, such as refining KG construction, exploring semantic similarity measures, enhancing the interlinking of legal information, and ensuring explainability and interpretability. These opportunities promise to enhance the efficiency and effectiveness of detecting overlapping laws and contribute to a more consistent legal system in Indonesia.

Page 260 of 291 | Total Record : 2901

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