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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 4: August 2024" : 75 Documents clear
Potentiality of graphene as a base material for impact ionization avalanche transit time diode in high-frequency applications Swain, Mamata Rani; Tripathy, Pravash Ranjan
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6860

Abstract

In this paper, the microwave application potential of graphene is studied using a double-drift-region (DDR) impact ionization avalanche transit time (IMPATT) diode. The simulation of this diode is carried out for the very first time at several different atmospheric window frequencies. Because graphene has unique and special properties, it could be used to make electronic gadgets for the next generation. The device is simulated at a variety of millimeter and sub-millimeter wave frequencies using a model called self-consistent drift diffusion (SCDD), which was developed by the author based on current continuity, Poisson’s equation and space charge equation. When compared to traditional IMPATT devices such as Si, GaAs, InP and GaP, the results demonstrate superior performance in terms of efficiency, and RF power across a wide range of operating conditions. Again, the behavior of noise in graphene IMPATT is studied, and it is found that it makes less noise than Si and GaAs IMPATT. The simulation results open up new avenues for IMPATT diode manufacture and design.
Customer data prediction and analysis in e-commerce using machine learning Al Rahib, Md Abdullah; Saha, Nirjhor; Mia, Raju; Sattar, Abdus
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6420

Abstract

Customer churn is a major challenge faced by e-commerce companies, as it leads to loss of revenue and decreased customer loyalty. In recent years, for predicting and reducing client churn machine learning techniques are powerful tools. This research aims to explore the use of machine learning algorithms for predicting customer churn, annual spending, and product on-time delivery in e-commerce. The study first conducted a comprehensive review of the literature on customer churn in machine learning. The literature showed that customer churn has been predicted successfully using a variety of machine learning algorithms, including support vector machine (SVM), random forest, and decision tree in various industries. To address this gap in the literature, the study conducted an empirical analysis of customer churn in e-commerce using machine learning algorithms. The data were then pre-processed and analyzed utilizing machine learning techniques for prediction. According to the study’s findings, machine learning algorithms are effective in predicting customer churn, and product on-time delivery in e-commerce. The best-performing algorithm SVM achieved an accuracy of 83.45% in predicting customer churn and 68.42% for product on-time delivery prediction.
Skin cancer classification using EfficientNet architecture Harahap, Mawaddah; Husein, Amir Mahmud; Kwok, Shane Christian; Wizley, Vincent; Leonardi, Jocelyn; Ong, Derrick Kenji; Ginting, Deskianta; Silitonga, Benny Art
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7159

Abstract

Skin cancer is one of the most common deadly diseases worldwide. Hence, skin cancer classification is becoming increasingly important because treatment in the early stages of skin cancer is much more effective and efficient. This study focuses on the classification of three common types of skin cancer, namely basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma using EfficientNet architecture. The dataset is preprocessed and each image in the dataset is resized to 256×256 pixels prior to incorporation in later stages. We then train all types of EfficientNet starting from EfficientNet-B0 to EfficientNet-B7 and compare their performances. Based on the test results, all trained EfficientNet models are capable of producing good accuracy, precision, recall, and F1-score in skin cancer classification. Particularly, our designed EfficientNet-B4 model achieves 79.69% accuracy, 81.67% precision, 76.56% recall, and 79.03% F1-score as the highest among others. These results confirm that EfficientNet architecture can be utilized to classify skin cancer properly.
Descriptive analysis of wide area network flow control internet traffic on Metro-E 100 Mbps campus network Abdullah, Nor Paezah; Kassim, Murizah; Mohd Deni, Sayang; Mohd Yussoff, Yusnani
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7044

Abstract

QoS in computer networking is the capability to provide better service to network traffic over various technologies such as ethernet and IP networks. This paper presents a descriptive analysis of WAN flow control and internet traffic on a Metro-E campus network. Issues on network congestion and delay in network QoS where internet traffic is gradually increasing, resulting in bursts of network capacity that affect network QoS. The method implies 12 months data collection and analysis on protocol, bytes and packets inbound and correlation between parameters on the Metro-E 100 Mbps campus network. The result presents heavy-tailed distributions on an inbound packet kurtosis value of 347 and an outbound packet kurtosis value of 780. Bytes outbound and inbound are skewed at 122 and right at 17 respectively. The average amount of data inbound and outbound is 458.5 MB and 34.8 MB. Protocol 6 TCP presents the highest amount of -traffic and a weak positive correlation at 0.104 exists between the inbound and outbound packets and bytes on the network. The correlation coefficient's 95% confidence interval ranges between 0.096 and 0.111. This research is significant in the future deployment of traffic scheduling, policing, and shaping algorithms for QoS bandwidth management on the WAN Metro-E campus network.
Streamlined multi-scenario revocation method leveraging blockchain and auxiliary trees Satish Babu, Battula Venkata; Babu, Kare Suresh; Kare, Durga Prasad
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7908

Abstract

Access revocation is a fundamental aspect of modern information systems, ensuring that data remains secure and authorized personnel have appropriate access rights. However, existing access revocation methods address only one type of scenario, offering either partial or complete revocation functionalities but not both, leading to limitations in flexibility and effectiveness. This paper introduces a novel approach called streamlined multi-scenario revocation method (SMSRM) that combines block chain technology and auxiliary trees to streamline the process of multi-scenario access revocation. The SMSRM method defines two separate revoke request formats for partial and complete revocation. Auxiliary trees are used to keep track of non-revoked users, which is very important during the revocation process. In addition, the proposed method utilizes a block chain to record each and every revocation-related operation to provide forward secrecy. Through a comparative analysis, we evaluate the performance of our approach against existing methods. The results highlight that our method performs better in terms of response time and various performance metrics.
A novel approach for e-health recommender systems Alsaaidah, Adeeb M.; Shambour, Qusai Y.; Abualhaj, Mosleh M.; Abu-Shareha, Ahmad Adel
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7749

Abstract

The increasing use of the internet for health information brings challenges due to the complexity and abundance of data, leading to information overload. This highlights the necessity of implementing recommender systems (RSs) within the healthcare domain, with the aim of facilitating more effective and precise healthcare-related decisions for both healthcare providers and users. Health recommendation systems can suggest suitable healthcare items or services based on users' health conditions and needs, including medications, diagnoses, hospitals, doctors, and healthcare services. Despite their potential benefits, RSs encounter significant limitations, including data sparsity, which can lead to recommendations that are unreliable and misleading. Considering the increasing significance of health recommendation systems and the challenge of sparse data, we propose an effective approach to improve precision and coverage in recommending healthcare items or services. This aims to assist users and healthcare practitioners in making informed decisions tailored to their unique needs and health conditions. Empirical testing on two healthcare rating datasets, including sparse datasets, illustrate that our proposed approach outperforms baseline recommendation methods. It excels in improving both the precision and coverage of health-related recommendations, demonstrating effective handling of extremely sparse datasets.
Swin transformer adaptation into YOLOv7 for road damage detection Irsal, Riyandi Banovbi Putera; Utaminingrum, Fitri; Ogata, Kohichi
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7556

Abstract

Highways are an important component of any country. However, some highways in Indonesia endanger users while maintaining road safety. Crack detection early in the deterioration process can prevent further damage and lower maintenance costs. A recent study sought to develop a method for detecting road damage by combining the road damage detection (RDD) dataset with generative adversarial network technology and data augmentation to improve training. The current study aims to broaden the you only look once (YOLO) framework by incorporating the Swin Transformer into the chiral stationary phases (CSP) component of YOLOv7, with the goal of improving object detection accuracy in a variety of visual scenarios. The study compares the performance of various object detection models with varying parameters and configurations, such as YOLOv5l, YOLOv6l, YOLOv7-tiny, YOLOv7, and YOLOv7x. YOLOv5l has 46 million parameters and 108 billion floating point operations per second (FLOPS), whereas YOLOv6l has 59.5 million parameters and 150 billion FLOPS. With 31 million parameters and 140 billion FLOPS, the YOLOv7-swin model performs best with mean average precision (mAP), mAP_0.50 of 0.47. and mAP_0.5:0.95 of 0.232. The experimental results show that our YOLOv7-swin model outperforms both YOLOv7x and YOLOv7-tiny. The proposed model significantly improves object detection accuracy while keeping complexity and performance in balance.
Shielding privacy: a technique of extenuating composition attacks in various independent data publication Faruq, Md. Omar; Walid, Md. Abul Ala; Baowaly, Mrinal Kanti; Devnath, Maloy Kumar; Ejaz, Md. Sabbir; Barman, Pronob Kumar; Sattar, A H M Sarowar
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6763

Abstract

Protecting personal information from unauthorized access is a critical concern for individuals. However, the accumulation of confidential information by various organizations, such as banks and hospitals, for regular communication creates a potential vulnerability. If an individual visits two hospitals and both facilities independently release the individual's gathered data, a malicious adversary could potentially deduce confidential information through a composition attack. Therefore, developing methods that protect individuals from composition attacks is crucial. According to the size of the dataset and the percentage of overlapping persons, our study examines the effectiveness of composition attacks. We propose a knowledge domain-based design to mitigate successful composition attacks, which has shown promising results in reducing such attacks and compared to existing studies based on the k-anonymity and l-diversity models. Our approach leverages a knowledge domain to reduce the likelihood of data breaches, demonstrating the effectiveness of our method in protecting individuals' privacy and preventing unauthorized access to sensitive information. Finally, the effects of data utility on the diverse data set have been measured.
A clustering method for energy efficient management of heterogeneous nodes of a flying ad hoc network Chaibi, Loubna; Sebgui, Marouane; Bah, Slimane
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6988

Abstract

The current paper presents a clustering method for energy efficient management of heterogeneous nodes of a flying ad hoc network (FANET). The technological advances of the last decade gave rise to emerging technologies. Unmanned aerial vehicles (UAVs) are small aircraft that proved their usefulness for different tasks nowadays. They can collaborate to achieve missions especially in areas where traditional networks cannot work or cannot accede. A FANET is composed by a number of these aircraft. For alike networks, the resources are limited. Indeed, an efficient energy management is required to extend the life of the network. This work is a clustering method for heterogeneous nodes of a FANET, each node is equipped with one sensor, and four different sensors are used. Clustering is grouping nodes with the aim of efficiency improvement. The clustering is done before the beginning of the rescue mission and depends on the types of sensors the nodes are equipped with and. The master election depends on the available energy of each one of the nodes. The simulation is done with a discrete event simulator (DES) and the results are compared to the algorithm of glowworm swarm optimization (GSO) to demonstrate the effectiveness of the suggested technique.
Internet of things and radio frequency identification based embedded system to reduce shopping time in supermarkets Espino, Cesar Solis; Vargas, Favio Guerrero; Paiva-Peredo, Ernesto; Segura, Guillermo Wenceslao Zarate
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7343

Abstract

Doing daily shopping in a Peruvian supermarket means a large investment of time for many people, usually due to inaccurate and faulty scanning of products by barcodes at supermarket checkout counters. For this reason, an embedded system based on internet of things (IoT) and radio frequency identification (RFID) is designed to reduce shopping time in a supermarket. The system uses an ESP32 development board with embedded hardware specialized in IoT projects and firmware development based on C language and real-time operating systems (FreeRTOS) through espressif’s IoT development framework (ESP-IDF). RFID tags were used to scan the products and IoT with message queuing telemetry transport (MQTT) communication protocol are implemented to a local database in real time. The system achieves a significant reduction in terms of scanning time compared to self-service checkouts using barcodes, which allows to statistically analyze the reduced time per quantity of products and the linear trend of the 2 samples.

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