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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 72 Documents
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Search results for , issue "Vol 13, No 1: February 2024" : 72 Documents clear
Malaysian views on COVID-19 vaccination program: a sentiment analysis study using Twitter Mohamed Ariff, Mohamed Imran; Shuhada Zubir, Nurul Erina; Azizan, Azilawati; Ahmad, Samsiah; Arshad, Noreen Izza
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6097

Abstract

This study aimed to analyze the opinions and emotions of Malaysians towards the COVID-19 vaccination program, as expressed on Twitter. By collecting data from the Twitter network and utilizing the machine learning life cycle technique. The results show that Malaysians have a mostly neutral viewpoint of the COVID-19 vaccination, with an accuracy score of 93%, an F1-score of 94%, a recall measurement of 94%, and a precision measure of 93%. These findings emphasize the significance of understanding public sentiment and perception towards crucial issues such as the COVID-19 vaccine and can be utilized to support healthcare professionals, policymakers, and the public in making informed decisions regarding the COVID-19 vaccination program.
IndoPolicyStats: sentiment analyzer for public policy issues Fakhruzzaman, Muhammad Noor; Jannah, Sa'idah Zahrotul; Gunawan, Sie Wildan; Pratama, Angga Iryanto; Ardanty, Denise Arne
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5263

Abstract

The government requires some vaccination for public health. This has led to a debate in recent years, especially during the Covid-19 pandemic. This research aims to analyze the two sentiments of the public regarding the vaccination policy. This would be helpful to ensure the acceptance of the government campaign about vaccination. The data used was text data obtained from Twitter when Indonesia was facing the second wave of the Covid-19 pandemic. The data were pre-processed by removing noise data, case folding, stemming, and tokenizing. Then, the data were classified with random forest, Naïve Bayes, and XGBoost. The results showed that all classifiers exhibit satisfying performance but XGBoost performs slightly better in accuracy value. This method can be deployed to be an automatic sentiment analyzer to help the government understand public feedback about its policies. This would be given by proper pre-processing and enough datasets.
Controlling a vehicle braking and longitudinal acceleration using a seeking control approach Salman, Saad A.; Shallal, Abidaoun H.; Sabry, Ahmad H.
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5639

Abstract

Traditional methods for tracking the paths of driverless vehicles use plant models to determine the corresponding control laws. Due to the intricate interactions between the road and the tires, time-varying characteristics, and unidentified disturbances. It is challenging to create an accurate vehicle model. As a result, data-driven controllers, which are independent of a predetermined plant model are becoming more and more well-liked. This work implements adaptive cruise control (ACC) by employing a control approach called extremum seeking technique (EST), which is a model-free control (MFC), to control a vehicle braking and longitudinal acceleration. The main aim here is to create an ego vehicle that travels at a specific speed with maintaining a secure space with respect to a guide vehicle. A car including an ACC technique called ego car, exploits radar to determine relative velocity and relative space relating to the guiding car. The ACC technique is considered to keep maintain a relatively secure space or a preferred cruising velocity concerning the guiding vehicle. The developed model succeeded to determine the relative velocity and relative space according for the ego car to another guiding car with acceleration not more than ±2 m/s2 and spacing error less than 6 m.
A deep learning-based intelligent decision-making model for tumor and cancer cell identification Durga, Putta; Godavarthi, Deepthi
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6469

Abstract

In the current era, the prevalence of common ailments is leading to an increasing number of fatalities. Various infections, viruses, and other pathogens can cause these illnesses. Some illnesses can give rise to tumors that seriously threaten human health. Distinct forms of tumors exist, including benign, premalignant, and malignant, with cancer being present only in malignant forms. Deep learning (DL) algorithms have emerged as one of the most promising methods for detecting cancers within the human body. However, existing models face criticism for their limitations, such as lack of support for large datasets, and reliance on a limited number of attributes from input images. To address these limitations and enable efficient cancer detection throughout the human body, an intelligent decision-making approach model (IDMA) is proposed. The IDMA is combined with the pre-trained VGG19 for improved training. The IDMA analyses convolutional neural network (CNN) layer images for signs of malignancy and rules out false positives. Various performance indicators, like sensitivity, precision, recall, and F1-score, are used to assess the system's performance. The suggested system has been evaluated and proven to outperform similar current systems, achieving an impressive 98.67% accuracy in detecting cancer cells.
Performance improvement of fuel cell and photovoltaic system Alagarsamy, Manjunathan; Chaudhary, Neera; Shanmugam, Nithyadevi; Suriyan, Kannadhasan; Loganathan, Arulmurugan; Manikandan, Raja
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.4250

Abstract

This article considers and studies a hybrid energy storage system as a potential replacement for a utility grid. It also examines its organisational structure. The hybrid energy storage technology is used to ensure a constant supply of convenient grid electricity that is sufficient to handle changing power spikes. Batteries are used to stabilise the surges with measurable variation, whereas a massive capacitor is utilised to stabilise the surges with fast variation. In isolated areas where connecting to the main utility grid is impractical, standalone renewable generation may provide the advantage of a reduced operational cost as well as a reduction in protection fees. In order to encourage non-conventional power production in the overall renewable energy system, advancements in solid oxide fuel-cell technology and solar photovoltaic (PV) technology have also been made. Grid-coupled solar PV energy producing systems are being extensively used worldwide, and solar PV modules are increasingly being used in residential applications linked to the electrical grid.
Based on deep convolutional neural network, COVID-19 identification utilizing computed tomography scans Yonan, Janan Farag; Fadheel, Fadil Raafat; Al-Doori, Mohammed A. J. Hammeid; Ali, Adnan Hussein
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5124

Abstract

In the year 2019 specifically, on March 11th, the coronavirus illness two thousand nineteen (COVID-19) was announced a worldwide epidemic due to its rapid spread and lack of treatment options. As a result, infected individuals must be identified and quarantined quickly to prevent the illness from spreading. The method used to test for COVID-19 is called real-time-polymerase chain reaction (RT-PCR), which has problems with having low sensitivity and taking an extended amount of time. Because chest computed tomography (CT) scans are more sensitive than RT-PCR, it follows that such scans can be employed for diagnostic purposes. This study developed a deep convolutional neural network (CNN) approach to detect COVID-19 using CT scan images. An architecture of deep learning (DL) called convolutional neural network computed tomography scans (CT-CNN) was utilized to efficiently identify COVID-19. The findings of our suggested model are highly encouraging, with an accuracy of 96.14%, an F1 score of 96.21%, and a recall of 97.53% when it comes to classifying CT scans as either infected or not infected by COVID-19.
TBNet: learning from scratch and limited training data, a CNN based tuberculosis bacilli detection Agoes, Ali Suryaperdana; Winarno, Winarno
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5279

Abstract

Tuberculosis (TB) is an infectious disease caused by the micro-bacteria. Several studies that have been conducted previously aimed to reduce the burden of observing tuberculosis bacilli using the digital image processing method. In this study, we proposed a newly developed convolutional neural network (CNN) based deep learning model to detect tuberculosis bacilli in sputum smear images. Recent advances in deep learning apply large scale image dataset to achieve convergent weight model. However, medical image dataset commonly available in relatively small quantity. In contrary with common deep learning approach, our model is capable to learn from our small dataset which consist of highly diverse hue and contrast of sputum smear images. Furthermore, its performance is proven to be reliable to detect sputum smear image content, which are TB bacillus and debris.
Frequency response of microgrids with PV power generation and energy storage system (battery and supercapacitor) Hamzah Abdullah, Alaa; M. Al-Anbary, Karrar; Mahdi Hamad, Qasim; Ali Al Abboodi, Hanaa Mohsin; Taih Gatte, Mohammed
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5071

Abstract

Since renewable energy sources (RES) have almost little inertia, an increase in their electricity might harm the power system's ability to run steadily and dependably. Numerous solutions to the issue mentioned above are offered. This paper aims to assess the technological possibility of using energy storage system (ESS) devices built from batteries and supercapacitors to enhance the interia response of sources in microgrids with a large amount of PV power penetration. The microgrid's inertia was altered by varying the penetration level of RES. To obtain a rigid microgrid, batteries and supercapacitors are suggested in this study to enhance frequency stability and droop control is utilized to complete this assessment. The model of the on-grid power network was designed using Simulink in MATLAB to evaluate the high level of RES penetration impact on the frequency stability of the system. Results verify that the microgrid stiffness is significantly enhanced when the suggested storage elements are incorporated. The findings show that the rate of change of frequency (RoCoF) is reduced when the size of the ESS increases and vice versa. The supercapacitor energy storage system (SCESS) can increase the stability of the system's frequency more effectively than the battery energy storage systems (BESS) with a slower time response.
An improvement for CAST-128 encryption based on magic square and matrix inversion Kareem, Suhad Muhajer; Al-Adhami, Ayad; S. Rahma, Abdul Monem
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5340

Abstract

This paper presents two image encryption methods which aim to improve the CAST 128-bit algorithm by increasing the security level of encrypted images. The first improvement uses a magic square of order three, while the second improvement uses a 2×2 matrix over GF(P). Both modifications are used in each round of the CAST algorithm, in place of a standard algorithm which uses XOR to increase the correlation between the plaintext and ciphertext. Simulations are carried out in order to evaluate the image encryption system with regard to complexity, time consumption, histogram, information entropy, differential attacks, noise evaluation, adjacent pixels’ correlation index, National Institute of Standards and Technology (NIST) analyses, mean absolute error, and average difference. The experimental results demonstrate that the encryption and decryption time when using the proposed CAST 128-bit algorithm with magic square is less than the time required for the CAST 128-bit algorithm with the matrix. Conversely, the proposed CAST with the matrix is higher than the CAST with the magic square. Both theoretical analysis and experimental results confirm that the two proposed enhancements to CAST perform effectively with sufficient security levels.
BER estimation for STBC-MC-DS-CDMA-4 antennas system by varied wavelet-carriers features via AWGN-flat channels Khadam, Nader Abdullah; Kadhim Jawad Alrubaie, Ali Jawad; Jumah Al-Thahab, Osama Qasim
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5371

Abstract

This paper improves the bit error rate (BER) of the modern communication system by taking into account the effect of the wavelet shape and the number of carriers on the performance of the space time block code multi-carrier direct sequence code division multiple access (STBC-MC-DS-CDMA). Here the transmitter is moved with speeds 2 km/hr, 45 km/hr and 100 km/hr via Rayleigh flat fading channel. Here, 2 antennas are employed at the receiver to mitigate the multipath signal influence. The system’s orthogonal frequencies are generated using Haar, Daubechies 4, Symlets 4, Cohen-Daubechies-Feauveau 1.1 with 9.7 and B-spline 3. The number of used carriers is 128, 512, and 1,024. Quadrature phase shift key (QPSK) is used with cyclic prefix 1/16 and a bandwidth of 20 MHz. traditional fast fourier transform (FFT) system is compared to the proposed discrete wavelet packet transform (DWPT) to show the BER enhancements. The space-time block coding (STBC) is used to enhance system cabability in error corection. The proposed system shows significant improvement in BER, so that, it reaches to the same BER when using FFT but with less signal to noise ratio (SNR), which interns reduce the power consumed within the system and the cost.

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