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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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Automatic spelling error detection and correction for Tigrigna information retrieval: a hybrid approach Desta, Solomon Gebremariam; Lehal, Gurpreet Singh
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4209

Abstract

This paper proposes a hybrid approach to design and implement query spelling error detection and correction (SEDC) for Tigrigna information retrieval (IR). Our approach, which is the main contribution to this work, is fast and robust to achieve better performance and also helps the users to easily insert their corrected queries to retrieve relevant information from the IR. This is achieved by combining the normalized measure of bigram overlap using the Jaccard coefficient (J.C) technique, a dynamic programming algorithm for edit distance, and probability of occurrence, which were used to make suggestions for the misspelt words. Our approach was evaluated on the SEDC subtasks separately. It achieved an F-measure of 98.85% on the spelling error detection subtask and an accuracy of 95.36% on the spelling error correction subtask. Thus, a comparison was conducted between our approach and the existing Tigrigna spell checker. It is found that our approach outperformed the existing spell checker and shows a 5.36% improvement in accuracy. This is by far the most promising result with regard to correcting the misspelt users’ queries and improving the overall performance of the IR.
Comparison of different methods to face the huge increase in future load in power distribution network Salam Bnyan Abood; Thamir M. Abdul Wahhab
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4092

Abstract

The main challenge of present distribution networks is the huge increase in demand for electricity especially in city center where the demand is increasing vertically for the same geographical area. This work presents the analyses of 5-Mail distribution network in Basra City/Iraq with conventional system 33/11/0.416 kV, at future load by estimating the increase in load 10 years later. The network is analyzed in terms of voltage drop, power losses, and the feeder loading. To improve the network the 33/11/0.416 kV system is re-analyzed at the expected future load using the optimal reconfiguration of the network or adding capacitor placement to reduce losses and voltage drop. The results of these methods are compared with the results of the network re-analyzed using the proposed 33/0.416 kV system at future load. The results show that the proposed method of upgrading the voltage level of distribution network is the best solution. The GIS software is used to locate the distribution transformers and lying of the underground cables. CYME software is used to simulate the electric distribution system and conduct the load flow and other analyses.The GIS software is used to locate the distribution transformers and lying of the underground cables. CYME software is used to simulate the electric distribution system and conduct the load flow and other analyses.
Cyberattacks and data breaches in Indonesia by Bjorka: hacker or data collector? Tole Sutikno; Deris Stiawan
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4854

Abstract

Recently, the public has been shocked by the mysterious figure of Hacker Bjorka. Bjorka hacked Indonesian officials. Bjorka leaks Indonesia's General Election Commission (KPU) data. This raises a significant red flag concerning Bjorka's ability to "disrupt" circumstances that are harmful to a large number of individuals, including his alleged action of leaking the personal data of influential state officials. Expert Putra Aji Adhari says Bjorka isn't a hacker. Aji Putra stated that Bjorka is a team. He, who has been invited to communicate with NASA, is sure Bjorka is still in Indonesia. Putra told Bjorka's hacking steps. Ardi Sutedja declared Bjorka isn't a person, his pattern mirrored a hacking group's. Sutedja knew Bjorka was Indonesian. Domestic targets, attacks, and mastery are evidences. On the other hand, Wiryana, as a hacker's handler, said that Bjorka is not a real hacker but rather a data collector. Ismail Fahmi says that a hacker like Bjorka uses a VPN to get to a server without leaving any traces. Bjorka might have come from Indonesia. One sign is that Bjorka's use of English is similar to how most Indonesians talk.
Characteristics with opposite of quranic letters mispronunciation detection: a classifier-based approach Tareq AlTalmas; Salmiah Ahmad; Nik Nur Wahidah Nik Hashim; Surul Shahbudin Hassan; Wahju Sediono
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3715

Abstract

Reading Quran for non-Arab is a challenge due to different mother tongues. learning Quran face-to-face is considered time-consuming. The correct pronunciation of Makhraj and Sifaat are the two things that are considered difficult. In this paper, Sifaat evaluation system was developed, focusing on Sifaat with opposites for teaching the pronunciation of the Quranic letters. A classifier-based approach has been designed for evaluating the Sifaat with opposites, using machine learning technique; the k-nearest neighbour (KNN), the ensemble random undersampling (RUSBoosted), and the support vector machine (SVM). Five separated classifiers were designed to classify the Quranic letters according to group of Sifaat with opposites, where letters that are classified to the wrong groups are considered mispronounced. The paper started with identifying the acoustic features to represent each group of Sifaat. Then, the classification method was identified to be used with each group of Sifaat, where best models were selected relying on various metrics; accuracy, recall, precision, and F-score. Cross-validation scheme was then used to protect against overfitting and estimate an unbiased generalization performance. Various acoustic features and classification models were investigated, however, only the outperformed models are reported in this paper. The results showed a good performance for the five classification models.
Simulating fog computing in OMNeT++ Noor Razaq Obaied Al-Rubaie; Rafal Nader Neamah Kamel; Raghda M. Alshemari
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4201

Abstract

Fog computing is a technology architecture in which data from IoT devices is received in real time by a number of nodes. These nodes process the data they receive in real time, with millisecond reaction times. The nodes communicate analytical summary data to the cloud on a regular basis. Fog computing scenario demands higher output, reduced latency, and greater performance as demand and requirements for improving performance in IoT applications grow. The resources allocation in effective manner in the fog environment is also a major problem in IoT-fog computing. Fog computing has been considered as a necessity within several IoT resources domains. In this paper the proposed fog simulation environment is focused on IoT sensors, fog node, and cloud as the used network architecture. However, the network features are properly explored in the proposed system and they are evaluated based on the throughput, latency, and channel allocation.
Detection of the patient with COVID-19 relying on ML technology and FAST algorithms to extract the features Seba Aziz Sahy; Sura Hammed Mahdi; Hassan Muwafaq Gheni; Israa Al-Barazanchi
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4355

Abstract

COVID-19 is unquestionably one of the most hazardous health issues of our century, and it is a significant cause of mortality for both men and women throughout the globe. Even with the most advanced pharmacological and technical innovations, cancer oncologists, and biologists still have a substantial problem treating COVID-19. For patients with COVID-19, it is critical to offer initial, precise, and effective indicative procedures to increase their survival and minimize morbidity and mortality, which is currently lacking. A COVID-19 detection method has been presented in this paper for the initial identification of COVID-19 hazard factors. Features from accelerated segment test (FAST), a robust feature was used to extract features in this suggested method. The experiments show that it is possible to identify FAST traits efficiently. A consequence was a high success rate (98%) for accuracy performance.
Towards classification of images by using block-based CNN Jasim, Retaj Matroud; Atia, Tayseer Salman
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4806

Abstract

Image classification is the process of assigning labeling to the input images to a fixed set of categories; however, assigning labels to the image is difficult by using the traditional method because of the large number of images. To solve this problem, we will resort to deep learning techniques. Which is enables computers to recognize and extract visual characteristics. The convolutional neural network (CNN) is a deep neural network used for many purposes, such as image classification, detection, and face recognition, due to its high-performance accuracy in classification and detection tasks. In this paper, we develop CNN based on the transfer learning approach for image classification. The network comprises two types of transfer learning, ResNet and DenseNet, as building blocks of the network with an multilayer perceptron (MLP) classifier. The proposed method does not need to preprocess before these datasets that input into the network. It was train on two datasets: the Cifar-10 and the Sign-Traffic datasets. We conclude that the proposed method achieves the best performance compared with other states of the art. The accuracy gained is 97.45% and 99.45%, respectively, where the proposed CNN increased the accuracy compared to other methods by 3%.
Design UWB antenna with notch band for WiMAX application Alaa Mohsen Ali; ِAli Khalid Jassim
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4104

Abstract

During the last two decades, radar, remote sensing, and imaging applications have all made use of ultra-wide band (UWB) technology. UWB systems are susceptible to interference from narrowband signals, hence this work provides a single-notch antenna for the UWB system. There are two stages to the design process. After creating the baseband antenna, it is necessary to create a notched band UWB antenna by carving a slot into patch antenna. In the UWB range (3.1-10.6) GHz, the UWB antenna has the dimensions of 20x30 mm with substrate thickness 1.6 mm made from FR4 lossy. The design relative permittivity was 4.3, a rectangular patch with a portion of the ground is used in the design. A typical slot-shaped resonator is connected to the patch to reject a frequency band (3.273-3.81) GHz which is a world interoperability for microwave access (WiMAX) to solve the problem of the interference with other bands in UWB system For WiMAX applications. The suggested UWB filter will achieve notch band response centered at the resonance frequency of 3,4 GHz. Analysis CSTS v2020 software was used to carry out the simulation. Priority should be given to what has been learned rather than what has been accomplished.
Statistical and machine learning approach for evaluation of control systems for automatic production lines Valentin Tsenev; Malinka Ivanova
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3664

Abstract

The manufacturing processes and the control systems for automatic production lines mainly are evaluated through usage of statistical methods as recently machine learning algorithms are also used. The aim of the paper is to present an approach for control measurement systems evaluation, based on a combination of statistical techniques like attribute repeatability and reproducibility analysis, measurement system analysis and supervised machine learning algorithms like random forest and KNN. The proposed method is verified in the production of the G8680x connector, which is used in the automotive industry. The control is performed 100% for all manufactured parts immediately after the “injection molding” process. It is proved that taking advantages of the statistics and machine learning, the manufacturing process and control measurement systems could be evaluated with very high accuracy. The exploration and analysis leads to the formulation of some recommendations in support of process engineers and managers.
A framework for predicting lncRNAs expression in human dendritic cells in response to M. tuberculosis infection Faizah Aplop; Saharuddin Mohamad
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4289

Abstract

Tuberculosis (TB) is an air-borne infectious diseases caused by M. tuberculosis bacteria that primarily affects human lungs. Existing vaccine does not work well due to the evolvement and latent movement of this bacteria. Developing an effective vaccine to combat Tuberculosis is very difficult as the interaction between the bacteria and human immune system is not fully understood. With recent advancement of transcriptome profiling analysis, long noncoding ribonukleat acids (lncRNAs) are found to be widely expressed in immune system. However, the role of lncRNAs is still not been widely explored in understanding human immune response to TB infection. In this paper, we propose a general framework for predicting lncRNAs being expressed in human dendritic cells. By incorporating deep learning method with RNA-seq data analysis, we intend to identify and characterize the lncRNAs found in dendritic cells from two groups of TB resistant patients through their RNA-seq expression data.

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