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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 1: February 2025" : 75 Documents clear
A scoring approach for detecting fake reviews using MRCS similarity metric enhanced by personalized k-means Ennaouri, Mohammed; Zellou, Ahmed
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8288

Abstract

Online commerce has grown in the digital age, and as a result, consumers now depend more than ever on other consumer feedback to make informed purchasing decisions. However, as the importance of reviews has increased, so has the prevalence of fake ones, which now infiltrate platforms and manipulate users' perceptions. This presents a significant challenge to preserving confidence and integrity in online marketplaces. This study addresses the difficulty of identifying fake reviews by introducing a distinctive methodology that incorporates advanced natural language processing (NLP) tools. By including a new metric, mean review cosine similarity (MRCS), which enhances textual similarity assessment for more accurate detection, we improve the identification process. Additionally, an exaggeration detection technique is included, enhancing the model's capacity to identify deceptive variations in review content. Furthermore, an adaptive clustering method differs from traditional k-means classification through modifying clusters to adjust to the constant evolution of misleading linguistic patterns. Empirical validation on the Yelp labeled dataset demonstrates the approach's accuracy (90%), with high precision (89%), recall (95%), and F1 score (92%), indicating its effectiveness and highlighting areas for further refinement.
Object detection in video surveillance using MobileNetV2 on resource-constrained low-power edge devices Lokhande, Harshad; R. Ganorkar, Sanjay
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8131

Abstract

Edge-based video surveillance systems encounter significant obstacles in object detection due to the limited computational power and energy efficiency of edge devices, which are required to deliver real-time processing capabilities. Traditional object detection models are excessively resource-hungry for these environments, making optimization techniques absolutely essential. This study robustly explores the implementation of quantized transfer learning utilizing SSD MobileNet V2 with 8-bit quantization to significantly elevate the performance of object detection on resource-constrained edge devices. Experimental results decisively indicate that the Raspberry Pi 5 and Nvidia Jetson Orin Nano surpass other devices, achieving total latencies of 5 ms and 85 ms, respectively, affirming their exceptional suitability for real-time applications. The quantized int8 model secures an impressive accuracy of 80.65% while dramatically lowering memory consumption and latency when compared to the unoptimized int32 model. Furthermore, the model demonstrates outstanding performance on a masked-unmasked dataset with an F1 score of 0.92 for masked detection. These findings underscore the transformative potential of quantization in enhancing both inference speed and resource efficiency in edge-based surveillance systems. Future research will boldly investigate advanced hybrid quantization strategies and architectural enhancements to achieve an optimal balance of efficiency and accuracy, alongside scalability across a broader spectrum of edge devices and datasets.
Enhanced student attendance and communication in educational management systems El Mustapha, Louragli; Gmih, Yassine; Soussi, Sohaib; Abdelmajid, Farchi
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7915

Abstract

The internet of things (IoT) and radio frequency identification (RFID) technology were explored to devise a beneficial approach for managing student attendance. The research developed a system that uses RFID tags embedded in student bracelets to gather presence data via strategically placed sensors. The system leverages real-time databases and Google technologies to enhance the student experience through an online platform, while also utilizing RFID for authentication. Focusing on improving user experience (UX) through effective design, the proposed system offers a pleasurable and cost-effective solution. Developed using popular web technologies such as Firebase, React.js, and Tailwind, along with Arduino chips and sensors, the system provides a practical solution for managing student attendance, academic performance, and administrative communication. The research highlights the potential of RFID technology in improving student management and academic performance. By decreasing the effort needed by traditional systems and proving cost-effective in the long term, it could act as a potential choice for implementation in educational institutions worldwide.
A new deep learning approach for predicting high-frequency short-term cryptocurrency price Akouaouch, Issam; Bouayad, Anas
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7377

Abstract

Cryptocurrencies are known for their volatility and instability, making them an attractive but risky investment for traders, analysts, and researchers. As the allure of Bitcoin (BTC) and other cryptocurrencies continues to grow, so does the interest in predicting their prices. To forecast the market rate and sustainability of cryptocurrencies, this study uses machine learning-based time series analysis. The study employs forecast periods ranging from 1 to 10-minutes to categorize the consistency of the market. High-frequency pricing of cryptocurrencies is anticipated with a timestep of up to 10 seconds using various deep learning (DL) models. A hybrid model combining long short-term memory (LSTM) and gated recurrent unit (GRU) is created and compared with standard LSTM and GRU models. Mean squared error (MSE) is the benchmark for estimating the models' performance. The study achieves better results than benchmark models, with MSE values for BTC, Cardano (ADA), and Cosmos (ATOM) in a 5-minute window size being 0.000192, 0.000414, and 0.000451, respectively, and for a 10-minute window size being 0.000212, 0.000197, and 0.000746. Compared to existing models, the suggested model offers a high price predicting accuracy. This study on crypto price prediction using machine learning applications is a preliminary investigation into the topic.
Development a decision support system for selection healthcare chatbot Phooriyaphan, Sirirak; Rachsiriwatcharabul, Natworapol
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8484

Abstract

It’s an increasing number of healthcare in many countries. Healthcare chatbot can save money, time and meet patient satisfaction. The healthcare would like to select the best or optimal healthcare chatbot but in the real situations, some healthcare may select the healthcare chatbot by own opinions in the organization with several criteria. The purpose of this research is to design and develop a decision support system (DSS) to select healthcare chatbot under the criteria of: i) functionalities; ii) multilingual ability; iii) usability; and iv) security and privacy. According to this research, it can help healthcare to make a reliable decision. The DSS allows users to select the most suitable alternatives of chatbot. The DSS is analyzed by using analytic hierarchy process (AHP). The result show that the DSS was designed to help in complex decision making and show the making decision of decision maker in the reliable and accurate decision. The result found that it is an appropriate technique for using in the DSS to select the suitable healthcare chatbot in accordance with overall criteria effectively including the sensitivity analysis.
Detection and prevention of Man-in-The-Middle attack in cloud computing using Openstack Tissir, Najat; Aboutabit, Noureddine; El Kafhali, Said
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8103

Abstract

This paper proposes a new technique designed to prevent and detect address resolution protocol (ARP) spoofing attacks in general, and specifically Man-in-the-Middle (MitM) attacks, within the context of cloud computing. The solution focuses on establishing appropriate flow filtering rules based on parameters such as 'time feature' and internet control message protocol '(ICMP) protocol'. The tests were conducted using the Openstack platform. One of the key benefits of this proposed approach is the improved performance in effectively detecting a significant number of malicious packets. We implemented this solution on the Openstack platform and conducted evaluations to demonstrate its efficacy. The results confirm that our method achieves superior performance in detecting MitM attacks, with a packet detection ratio (PDR) of 60.4%. Moving forward, this work will contribute to protecting cloud environments from a large number of MitM attacks.
Hyperparameter tuning for deep learning model used in multimodal emotion recognition data Widardo, Fernandi; Chowanda, Andry
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8707

Abstract

This study attempts to address overfitting, a frequent problem with multimodal emotion identification models. This study proposes model optimization using various hyperparameter approaches, such as dropout layer, l2 kernel regularization, batch normalization, and learning rate schedule, and discovers which approach yields the most impact for optimizing the model from overfitting. For the emotion dataset, this research utilizes the interactive emotional dyadic motion capture (IEMOCAP) dataset and uses the motion capture and speech audio data modality. The models used in this experiment are convolutional neural network (CNN) for the motion capture data and CNN-bidirectional long short-term memory (CNN-BiLSTM) for the audio data. This study also applied a smaller model batch size in the experiment to accommodate the limited computing resources. The result of the experiment is that the optimization using hyperparameter tuning raises the validation accuracy to 73.67% and the f1-score to 73% on audio and motion capture data, respectively, from the base model of this research and can competitively compete with another research model result. It is hoped that the optimization experiment results in this study can be useful for future emotion recognition research, especially for those who have encountered overfitting problems.
Development and experimental study of an intelligent water quality monitoring system based on the internet of things Amirgaliyev, Beibut; Kozbakova, Ainur; Omarova, Perizat; Merembayev, Timur; Amirzhan, Kanat
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8864

Abstract

The goal of this work is to create an intelligent internet of things (IoT)-based water quality monitoring system that will effectively monitor and analyze water parameters, collect real-time data, and provide critical information for decision-making in water management and environmental issues. Provide data transfer over wireless networks such as Wi-Fi or Bluetooth. The scientific novelty of the project lies in the development of an innovative system that combines modern IoT technologies and machine learning methods to provide comprehensive and accurate water quality monitoring, which is a significant contribution to water management and environmental safety. Five sensors are connected to Arduino-Mega 2560, ESP-32-E in a discrete manner to determine water parameters. The extracted sensor data is transferred to a desktop application developed on the Blynk App platform and compared with World Health Organization (WHO) standard values. Based on the measurement results, the proposed system can successfully analyze water parameters using the fast forest binary classifier to determine whether the tested water sample is potable or not. An intuitive user interface has been created that will allow users to monitor and analyze water quality data in real time. Provide the ability to create graphs, charts, and reports for visual presentation of data.
Modern artificial intelligence technics for unmanned aerial vehicles path planning and control Zamoum, Yasmine; Baiche, Karim; Benkeddad, Youcef; Bouzida, Brahim; Boushaki, Razika
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8376

Abstract

Unmanned aerial vehicles (UAVs) require effective path planning algorithms to navigate through complex environments. This study investigates the application of Deep Q-learning and Dyna Q-learning methods for UAV path planning and incorporates fuzzy logic for enhanced control. Deep Q-learning, a reinforcement learning technique, employs a deep neural network to approximate Q-values, allowing the UAV to improve its path planning capabilities by maximizing cumulative rewards. Conversely, Dyna Q-learning leverages simulated scenarios to update Q-values, refining the UAV’s decision-making process and adaptability to dynamic environments. Additionally, fuzzy logic control is integrated to manage UAV movements along the planned path. This control system uses linguistic variables and fuzzy rules to handle uncertainties and imprecise information, enabling real-time adjustments to speed, altitude, and heading for accurate path following and obstacle avoidance. The research evaluates the effectiveness of these methods individually, with a focus on model-free learning in a gradual training approach, and compares their performance in terms of path planning accuracy, adaptability, and obstacle avoidance. The paper contributes to a deeper understanding of UAV path planning techniques and their practical applications in various scenarios.
Optimizing earthquake damage prediction using particle swarm optimization-based feature selection Anisa Sri Winarsih, Nurul; Anggi Pramunendar, Ricardus; Fajar Shidik, Guruh; Widjajanto, Budi; Syaifur Rohman, Muhammad; Oka Ratmana, Danny
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8421

Abstract

Earthquakes have destroyed the economy and killed many people in many countries. Emergency response actions immediately after an earthquake significantly reduce economic losses and save lives, so accurate earthquake damage predictions are needed. This research looks at how machine learning (ML) techniques are used to predict damage from earthquakes. The ML algorithms used are k-nearest neighbors (KNN), decision tree (DT), random forest (RF), and Naïve Bayes (NB). Feature selection is necessary, it needs to select the most relevant features from big data. One of the most commonly used algorithms to optimize ML is particle swarm optimization (PSO). PSO is also suitable for feature selection. This research compares various of PSO. Based on research, the RF algorithm with Phasor PSO has the highest fitness score. This process succeeded in reducing features from 38 features to 14 features. Based on the process after feature selection, it was found that the KNN, DT, and RF algorithms had improved. RF obtained the best accuracy, namely 72.989%. The processing time in DT, RF, and NB is faster than before. In conclusion, the ML algorithm can be combined with PSO feature selection to create a classification model that provides better performance than without feature selection.

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