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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.
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Articles 75 Documents
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Search results for , issue "Vol 13, No 5: October 2024" : 75 Documents clear
ABSA of Indonesian customer reviews using IndoBERT: single- sentence and sentence-pair classification approaches Yulianti, Evi; Nissa, Nuzulul Khairu
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.8032

Abstract

Aspect-based sentiment analysis (ABSA) task is important to identify user satisfaction from customer reviews by recognizing the sentiments of all aspects discussed in the reviews. This work investigates a novel study on the effectiveness and efficiency of three IndoBERT-based models for solving the ABSA task in Indonesian language. IndoBERT is a state-of-the-art transformer-based model, i.e., bidirectional encoder representations from transformers (BERT), that was pre-trained on Indonesian language. Our first model utilizes IndoBERT in a feature-based mode, paired with the convolutional neural network (CNN) and machine learning models, for single-sentence classification. Next, our second model is obtained by fine- tuning the IndoBERT model for a typical single-sentence classification to build an end-to-end model. At last, our third model also adopts a fine-tuning approach to use IndoBERT, but for sentence-pair classification by utilizing auxiliary sentences. Our results demonstrate that the third model, the fine- tuned IndoBERT for sentence-pair classification, gains the highest effectiveness. It demonstrates significant improvement over deep learning baselines (Word2Vec-CNN-XGBoost) by 23.6% and transformer-based baselines (mBERT-aux-NLIB) by 2.2% in terms of F-1 score. When considering both effectiveness and efficiency, the results show that the best- performing model is our second model, the fine-tuned IndoBERT for single- sentence classification.
Application of neural networks ensemble method for the Kazakh sign language recognition Amirgaliyev, Yedilkhan; Ataniyazova, Aisulyu; Buribayev, Zholdas; Zhassuzak, Mukhtar; Urmashev, Baydaulet; Cherikbayeva, Lyailya
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7803

Abstract

Sign languages are an extremely important means of communication in many cases, especially for deaf and hard of hearing people. But the same gesture can convey different meanings in different countries, so many different sign languages have been developed all over the world. In this study, a convolutional neural network (CNN) model was developed based on an ensemble method containing the ResNet-50 and VGG-19 architectures, which will be able to classify the Kazakh sign language (KSL) consisting of 42 Kazakh alphabet signs (classes). A proprietary data set of 57,708 images for 42 signs of the KSL has been formed. The ensemble model was compared with ResNet-50 and VGG-19 by evaluation metrics such as accuracy, precision, recall, f1-measure, and loss function. The recognition accuracy of the ensemble method reached 95.7%, exceeding the performance of ResNet-50 and VGG-19. The developed method was also tested on test data, where 35 out of 42 gestures were recognized completely correctly. The reliability of the proposed approach and the classification results obtained by using preprocessing methods and data augmentation techniques to expand the data set was confirmed by a computational experiment.
The potential of light fidelity in smart home automation Aydin, Hakan; Aydın, Gülsüm Zeynep Gürkaş; Aydın, Muhammed Ali
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7199

Abstract

Light fidelity (Li-Fi) is a pioneering optical wireless communication (OWC) technology that utilizes visible light for wireless data transmission. Since its inception in a TED global talk by Professor Harald Haas in 2011, Li-Fi has captured significant attention in the research community. Smart home automation systems (SHAs) leverage internet of things (IoT) technology to remotely manage and automate various home devices and systems. Li-Fi technology has the potential to enable remote control of devices such as lighting, air conditioning, music systems, security cameras, and door locks within SHAs. This study presents Li-Fi-IoT, a Li-Fi-based system designed for efficient and secure IoT device management in SHAs. A series of experiments demonstrates the system's potential in IoT device control using Li-Fi technology. The research findings highlight the substantial improvement in data transfer speed, energy efficiency, and data security that Li-Fi technology can bring to SHAs.
Developing a secure voice recognition service on Raspberry Pi Le, Van-Hoan; Luc, Nhu-Quynh; Quach, Duc-Huy
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7655

Abstract

In this study, we present a novel voice recognition service developed on the Raspberry Pi 4 model B platform, leveraging the fast Fourier transform (FFT) for efficient speech-to-digital signal conversion. By integrating the hidden Markov model (HMM) and artificial neural network (ANN), our system accurately reconstructs speech input. We further fortify this service with dual-layer encryption using the Rivest–Shamir–Adleman (RSA) and advanced encryption standard (AES) methods, achieving encryption and decryption times well suited for real-time applications. Our results demonstrate the system's robustness and efficiency: speech processing within 1.2 to 1.9 seconds, RSA 2048-bit encryption in 2 to 6 milliseconds, RSA decryption in 6 to 10 milliseconds, and AES-GCM 256-bit encryption and decryption in approximately 2.6 to 3 seconds.
XSSer: hybrid deep learning for enhanced cross-site scripting detection Odeh, Ammar; Abu Taleb, Anas
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7905

Abstract

The importance of an effective cross-site scripting (XSS) detection system cannot be overstated in web security. XSS attacks continue to be a prevalent and severe threat to web applications, making the need for robust detection systems more crucial than ever. This paper introduced a hybrid model that leverages deep learning algorithms, combining recurrent neural network (RNN) and convolutional neural network (CNN) architectures. Our hybrid RNN-CNN model emerged as the top performer in our evaluation, demonstrating outstanding performance across key metrics. It achieved an impressive accuracy of 96.74%, excelling inaccurate predictions. Notably, the precision score reached an impressive 97.78%, highlighting its precision in identifying positive instances while minimizing false positives. Furthermore, the model's recall score of 95.65% showcased its ability to capture a substantial portion of true positive instances. This resulted in an exceptional F1-Score of 96.70, underlining the model's remarkable balance between precision and recall. Compared to other models in the evaluation, our proposed model unequivocally demonstrated its leadership, emphasizing its excellence in detecting potential XSS vulnerabilities within web content.
Robust sliding mode observer based-simultaneous state and actuator fault estimation for a class of switching systems Elouni, Mohamed; Hamdi, Habib; Rabaoui, Bouali; Rodrigues, Mickael; Benhadj Braiek, Naceur
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.6676

Abstract

This paper investigates the state and actuator fault reconstruction problem in a class of switched linear systems subjected to unknown external disturbances according to average dwell time (ADT) technique. First, a robust switched sliding mode observer (SMO) is developed to simultaneously reconstruct the states of the switched system and the actuator faults. A novel and less conservative sufficient stability conditions are then established using the multiple quadratic Lyapunov function technique and the ADT approach. These conditions are formulated as linear matrix inequalities (LMI) to facilitate the design of the SMO. The observer gains matrices are obtained throughout the resolution of LMI using convex optimization techniques. Next, actuator faults are estimated by utilizing the concept of equivalent output injection, achieved through an analysis of the state error dynamics during the sliding motion. Finally, simulation results are considered to illustrate the applicability and efficiency of the developed method. It showcases the rapid and accurate convergence of the estimated system states and actuator fault to the real variables.
Energy efficiency based RPL protocol using grasshopper optimization algorithm Matada Murigendraiah, Savitha; I. Basarkod, Prabhugoud
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7856

Abstract

The routing protocol for low-power and lossy networks (RPL) is necessary for the internet of things (IoT) because it offers scalable, reliable, and energy-efficient routing capabilities. The trickling algorithm generates a destination-oriented directed acyclic graph (DODAG) with the broadcasting of suppression. However, broadcast suppression is insufficient when addressing network coverage and optimization problems based on uneven node distribution. Network congestion develops in large-scale IoT implementations where many devices are interconnected and congestion causes data transmission delays, decreased overall reliability, and higher latency. In this paper, the grasshopper optimization algorithm with the DODAG (GOA-DODAG) is proposed to determine optimization problems and energy-efficient reliable routing paths which include coverage-based dynamic trickling technique to construct DODAG energy-efficient without affecting the coverage of network and data routing reliability. The GOA-DODAG achieves a 98% packet delivery ratio (PDR) while consuming 0.48 mJ, which is more preferable in comparison to the existing methods like efficient-routing protocol for low-power and lossy networks (E-RPL), reliable and energy-efficient RPL (REFER), elaborated cross-layer RPL objective function to achieve energy efficiency (ELITE).
Multimodal speech emotion recognition optimization using genetic algorithm Michael, Stefanus; Zahra, Amalia
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7409

Abstract

Speech emotion recognition (SER) is a technology that can detect emotions in speech. Various methods have been used in developing SER, such as convolutional neural networks (CNNs), long short-term memory (LSTM), and multilayer perceptron. However, sometimes in addition to model selection, other techniques are still needed to improve SER performance, namely optimization methods. This paper compares manual hyperparameter tuning using grid search (GS) and hyperparameter tuning using genetic algorithm (GA) on the LSTM model to prove the performance increase in the multimodal SER model after optimization. The accuracy, precision, recall, and F1 score improvement obtained by hyperparameter tuning using GA (HTGA) is 2.83%, 0.02, 0.05, and 0.04, respectively. Thus, HTGA obtains better results than the baseline hyperparameter tuning method using a GS.
Improved car detection performance on highways based on YOLOv8 Sutikno, Sutikno; Sugiharto, Aris; Kusumaningrum, Retno; Wibawa, Helmie Arif
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.8031

Abstract

Car detection on the road through computer vision is crucial for improving safety, as it plays an essential role in spotting nearby vehicles and preventing fatal accidents. Additionally, car detection significantly contributes to the advancement of autonomous vehicles. Previous explorations of car detection using YOLOv5 have revealed weaknesses regarding its resulting mean average precision (mAP). This scenario led to the development of a more advanced version of you only look once (YOLO), namely YOLOv8. Consequently, this study aimed to adopt YOLOv8 for automatic car detection on the road. YOLOv8 is proven to perform better than the previous version. A dataset comprising video frame images was captured on the highway in Semarang, Indonesia. The experiment results indicated that the proposed approach achieved impressive precision, recall, and mAP values, reaching 94.1%, 98.2%, and 98.8%, respectively. The proposed approach enhanced mAP and training time when compared with YOLOv5. Therefore, it was concluded that the proposed method was better suited for real-time car detection.
Differential evolution with adaptive mutation and crossover strategies for nonlinear regression problems Wongsa, Watchara; Puphasuk, Pikul; Wetweerapong, Jeerayut
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.6417

Abstract

This paper presents the differential evolution algorithm with adaptive mutation and crossover strategies (DEAMC) for solving nonlinear regression problems. The DEAMC algorithm adaptively uses two mutation strategies and two ranges of crossover rate. We evaluate its performance on the National Institute of Standards and Technology (NIST) nonlinear-regression benchmark containing many models of varying levels of difficulty and compare it with classic differential evolution (DE), enhanced differential evolution algorithm with an adaptation of switching crossover strategy (DEASC), and controlled random search methods (CRS4HC, CRS4HCe). We also apply the proposed method to solve parameter identification applications and compare it with enhanced chaotic grasshopper optimization algorithms (ECGOA), self-adaptive differential evolution with dynamic mutation and pheromone strategy (SDE-FMP), and JAYA and its variant methods. The experimental results show that DEAMC is more reliable and gives more accurate results than the compared methods.

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