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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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An optimal placement of phasor measurement unit using new sensitivity indices K. Khalid; A. A. Ibrahim; N. A. M. Kamari; M. H. M. Zaman
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2499

Abstract

This paper presents an alternative approach to solve an optimal phasor measurement unit (PMU) placement by introducing two new sensitivity indices. The indices are constructed from the information measured from PMUs such as voltage magnitude and angle. These two parameters are essential for power system stability assessment and control. Therefore, fault analysis is carried out to obtain the voltage magnitude and angle deviations at all buses in order to construct the indices. An exhaustive search method is used to solve the linear integer programming problem where all possible combinations of PMU placement are evaluated to obtain the optimal solution. Unfortunately, the traditional objective function to minimize the total number of PMU placement leads to multiple solutions. The indices can be used to assess multiple solutions of PMU placement from the exhaustive method. In this work, an optimal solution is selected based on the performance of PMU placement in according to the indices. The proposed method is tested on the IEEE 14 bus test system. Only four PMUs are required to monitor the whole test system. However, the number of PMUs can be reduced to three PMUs after applying zero injection bus elimination.
Research trends review on RSA scheme of asymmetric cryptography techniques Mohd Saiful Adli Mohamad; Roshidi Din; Jasmin Ilyani Ahmad
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2493

Abstract

One of the cryptography classifications is asymmetric cryptography, which uses two different keys to encrypt and decrypt the message. This paper discusses a review of RSA scheme of asymmetric cryptography techniques. It is trying to present the domains of RSA scheme used including in public network, wireless sensor network, image encryption, cloud computing, proxy signature, Internet of Things and embedded device, based on the perspective of researchers’ effort in the last decade. Other than that, this paper reviewed the trends and the performance metrics of RSA scheme such as security, speed, efficiency, computational complexity and space based on the number of researches done. Finally, the technique and strengths of the proposed scheme are also stated in this paper. 
“Scrumbear” framework for solving traditional scrum model problems Leanah Alsaber; Ebtesam Al Elsheikh; Sarah Aljumah; Nor Shahida Mohd Jamail
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2487

Abstract

Software engineering is a discipline that is little understood by people. It defines how software is developed and maintained to meet the clients’ requirements. Software engineers follow certain systems and standards in their work to meet the clients’ desires. It is on this background that engineers follow specific models in coming up with the final product. One of the models highly used is scrum, which is one of the agile methodologies. However, despite being highly used, it has inherent flaws that need to be corrected. Those flaws are product owner continues changing; do not accept changes in working scrum, sprint’s release time limitation, finally wasting team time within each sprint. This paper presents a new framework, which is an updated version of the current Scrum, to overcome the scum models mentioned issues. In this study, a new framework is presented in a way that is understandable and needed by software developer’s team upon the collected qualitative and quantitative data. The implementation was by making some changes to the current scrum model leading to the “Scrumbear”, certain flaws can be corrected. One of the presented changes involve adding the control master rule to ensure controlling the requirements changing.
Multischeme feedforward artificial neural network architecture for DDoS attack detection Arif Wirawan Muhammad; Cik Feresa Mohd Foozy; Kamaruddin Malik bin Mohammed
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2383

Abstract

Distributed denial of service attack classified as a structured attack to deplete server, sourced from various bot computers to form a massive data flow. Distributed denial of service (DDoS) data flows behave as regular data packet flows, so it is challenging to distinguish between the two. Data packet classification to detect DDoS attacks is one solution to prevent DDoS attacks and to maintain server resources maintained. The machine learning method especially artificial neural network (ANN), is one of the effective ways to detect the flow of data packets in a computer network. Based on the research that has carried out, it concluded that ANN with hidden layer architecture that contains neuron twice as neuron on the input layer (2n) produces a stable detection accuracy value on Quasi-Newton, scaled-conjugate and resilientpropagation training functions. Based on the studies conducted, it concluded that ANN architecture sufficiently affected the scaled-conjugate and resilient-propagation training functions, otherwise the Quasi-Newton training function. The best detection accuracy achieved from the experiment is 99.60%, 1.000 recall, 0.988 precision, and 0.993 f-measure using the quasinewton training function with 6-(12)-2 neural network architecture.
Towards an objective comparison of feature extraction techniques for automatic speaker recognition systems Ayoub Bouziane; Jamal Kharroubi; Arsalane Zarghili
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.1782

Abstract

A common limitation of the previous comparative studies on speaker-features extraction techniques lies in the fact that the comparison is done independently of the used speaker modeling technique and its parameters. The aim of the present paper is twofold. Firstly, it aims to review the most significant advancements in feature extraction techniques used for automatic speaker recognition. Secondly, it seeks to evaluate and compare the currently dominant ones using an objective comparison methodology that overcomes the various limitations and drawbacks of the previous comparative studies. The results of the carried out experiments underlines the importance of the proposed comparison methodology. 
Reconfiguration layers of convolutional neural network for fundus patches classification Wahyudi Setiawan; Moh. Imam Utoyo; Riries Rulaningtyas
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.1974

Abstract

Convolutional neural network (CNN) is a method of supervised deep learning. The architectures including AlexNet, VGG16, VGG19, ResNet 50, ResNet101, GoogleNet, Inception-V3, Inception ResNet-V2, and Squeezenet that have 25 to 825 layers. This study aims to simplify layers of CNN architectures and increased accuracy for fundus patches classification. Fundus patches classify two categories: normal and neovascularization. Data used for classification is MESSIDOR and Retina Image Bank that have 2,080 patches. Results show the best accuracy of 93.17% for original data and 99,33% for augmentation data using CNN 31 layers. It consists input layer, 7 convolutional layers, 7 batch normalization, 7 rectified linear unit, 6 max-pooling, fully connected layer, softmax, and output layer.
Selection of optimal hyper-parameter values of support vector machine for sentiment analysis tasks using nature-inspired optimization methods Lakshmana Kumar Ramasamy; Seifedine Kadry; Sangsoon Lim
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2098

Abstract

Sentiment analysis and classification task is used in recommender systems to analyze movie reviews, tweets, Facebook posts, online product reviews, blogs, discussion forums, and online comments in social networks. Usually, the classification is performed using supervised machine learning methods such as support vector machine (SVM) classifier, which have many distinct parameters. The selection of the values for these parameters can greatly influence the classification accuracy and can be addressed as an optimization problem. Here we analyze the use of three heuristics, nature-inspired optimization techniques, cuckoo search optimization (CSO), ant lion optimizer (ALO), and polar bear optimization (PBO), for parameter tuning of SVM models using various kernel functions. We validate our approach for the sentiment classification task of Twitter dataset. The results are compared using classification accuracy metric and the Nemenyi test.
An efficient apriori algorithm for frequent pattern mining using mapreduce in healthcare data M. Sornalakshmi; S. Balamurali; M. Venkatesulu; M. Navaneetha Krishnan; Lakshmana Kumar Ramasamy; Seifedine Kadry; Sangsoon Lim
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2096

Abstract

The development for data mining technology in healthcare is growing today as knowledge and data mining are a must for the medical sector. Healthcare organizations generate and gather large quantities of daily information. Use of IT allows for the automation of data mining and information that help to provide some interesting patterns which remove manual tasks and simple data extraction from electronic records, a process of electronic data transfer which secures medical records, saves lives and cuts the cost of medical care and enables early detection of infectious diseases. In this research paper an improved Apriori algorithm names enhanced parallel and distributed apriori (EPDA) is presented for the health care industry, based on the scalable environment known as Hadoop MapReduce. The main aim of the work proposed is to reduce the huge demands for resources and to reduce overhead communication when frequent data are extracted, through split-frequent data generated locally and the early removal of unusual data. The paper shows test results, whereby the EPDA performs in terms of the time and number of rules generated with a database of healthcare and different minimum support values.
A novel technique for speech encryption based on k-means clustering and quantum chaotic map Amal Hameed Khaleel; Iman Qays Abduljaleel
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2405

Abstract

In information transmission such as speech information, higher security and confidentiality are specially required. Therefore, data encryption is a pre-requisite for a secure communication system to protect such information from unauthorized access. A new algorithm for speech encryption is introduced in this paper. It depends on the quantum chaotic map and k-means clustering, which are employed in keys generation. Also, two stages of scrambling were used: the first relied on bits using the proposed algorithm (binary representation scrambling BiRS) and the second relied on k-means using the proposed algorithm (block representation scrambling BlRS). The objective test used statistical analysis measures (signal-to-noise-ratio, segmental signal-to-noise-ratio, frequency-weighted signal-to-noise ratio, correlation coefficient, log-likelihood ratio) applied to evaluate the proposed system. Via MATLAB simulations, it is shown that the proposed technique is secure, reliable and efficient to be implemented in secure speech communication, as well as also being characterized by high clarity of the recovered speech signal.
Design of 4-stage Marx generator using gas discharge tube Wijono Wijono; Zainul Abidin; Waru Djuriatno; Eka Maulana; Nola Ribath
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.1949

Abstract

In this paper, a Marx generator voltage multiplier as an impulse generator made of multi-stage resistors and capacitors to generate a high voltage is proposed. In order to generate a high voltage pulse, a number of capacitors are connected in parallel to charge up during on time and then in series to generate higher voltage during off period. In this research, a 6kV Marx generator voltage multiplier is designed using gas discharge tube (GDT) as an electronic switch to breakdown voltage. The Marx generator circuit is designed to charge the storage capacitor for high impulse voltage and current generator applications. According to IEC 61000-4-5 class 4 standards, the storage capacitor must be charged up to 4 kV. The results show that the proposed Marx generator can produce voltages up to 6.8 kV. However, the storage capacitor could be charged up to 1 kV, instead of 4 kV in the standard. That is because the output impulse voltage has narrow time period.

Page 89 of 291 | Total Record : 2901

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