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Bulletin of Electrical Engineering and Informatics
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Core Subject : 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 63 Documents
Search results for , issue "Vol 10, No 4: August 2021" : 63 Documents clear
Spell corrector for Bangla language using Norvig’s algorithm and Jaro-Winkler distance Istiak Ahamed; Maliha Jahan; Zarin Tasnim; Tajbia Karim; S. M. Salim Reza; Dilshad Ara Hossain
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.2410

Abstract

In the online world, especially in the social media platform most of us write without much regard to correct spelling and grammar. The spelling mistakes are much larger in proportion when it comes to Bangla language. In our paper, we presented a method for error detection and correction in Bangla words' spellings. Our system could detect a misspelled Bangla word and provide two following services-suggesting correct spellings for the word and correcting the word. We had used Norvig's algorithm for the purpose but instead of using probabilities of the words to prepare the suggestions and corrections, we had used Jaro-Winkler distance. The previous works done in this field for Bangla language are either very slow or offers less accuracy. Our system successfully achieved a 97% accuracy when evaluated with 1000 Bangla words.
A multi domains short message sentiment classification using hybrid neural network architecture Devi Munandar; Andri Fachrur Rozie; Andria Arisal
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.2790

Abstract

Sentiment analysis of short texts is challenging because of its limited context of information. It becomes more challenging to be done on limited resource language like Bahasa Indonesia. However, with various deep learning techniques, it can give pretty good accuracy. This paper explores several deep learning methods, such as multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and builds combinations of those three architectures. The combinations of those three architectures are intended to get the best of those architecture models. The MLP accommodates the use of the previous model to obtain classification output. The CNN layer extracts the word feature vector from text sequences. Subsequently, the LSTM repetitively selects or discards feature sequences based on their context. Those advantages are useful for different domain datasets. The experiments on sentiment analysis of short text in Bahasa Indonesia show that hybrid models can obtain better performance, and the same architecture can be directly used in another domain-specific dataset.
Design a mobile application for vehicles managing of a transportation issue Seddiq Q. Abd Al-Rahman; Sameeh Abdulghafour Jassim; Ali Makki Sagheer
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.2918

Abstract

The movement of people between cities is leading to a recovery in the economy that transportation companies have begun to dominate. These companies start providing the best services to customers and promoting them through workers to earn money properly. From this basis, this paper presents a system designed to manage a company that transports people and goods between a group of cities. Database management was used across the web to enable data exchange between workers. The database is designed to be accessible to workers. It has also been suggested that the elliptic curve can be used to generate public and private keys for all parties while the company's management generates a prime number every day to ensure the confidentiality of the exchanged data. In this proposal, the rivest-shamir-adleman (RSA) algorithm is used to encrypt transferred data. It uses technology to exchange information if the recipient is not connected to the network. The proposed system performs a good service for the company’s management in securing the transferred data where smartphone applications are designed to work on it.
Image confusion and diffusion based on multi-chaotic system and mix-column Amal Abdulbaqi Maryoosh; Zahraa Salah Dhaif; Raniah Ali Mustafa
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.2942

Abstract

In this paper, a new image encryption algorithm based on chaotic cryptography was proposed. The proposed scheme was based on multiple stages of confusion and diffusion. The diffusion process was implemented twice, first, by permuting the pixels of the plain image by using an Arnold cat map and, the second time by permuting the plain image pixels via the proposed permutation algorithm. The confusion process was performed many times, by performing the XOR operation between the two resulted from permuted images, subtracted a random value from all pixels of the image, as well as by implementing the mix column on the resulted image, and by used the Lorenz key to obtain the encrypted image. The security analysis tests that used to exam the results of this encryption method were information entropy, key space analysis, correlation, histogram analysis UACI, and NPCR have shown that the suggested algorithm has been resistant against different types of attacks.
Robotic arm joint position control using iterative learning and mixed sensitivity H∞ robust controller Petrus Sutyasadi; Martinus Bagus Wicaksono
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.3059

Abstract

This paper proposes an improved control strategy of a robotic arm joint using hybrid controller consist of H∞ robust controller and iterative learning controller. The main advantage of this controller is the simple structure that made it possible to be implemented on a small embedded system for frugal innovation in industrial robotic arm development. Although it has a simple structure, it is a robust H∞ controller that has robust stability and robust performance. The iterative learning controller makes the trajectory tracking even better. To test the effectiveness of the proposed method, computer simulations using Matlab and hardware experiments were conducted. Variation of load was applied to both of the processes to present the uncertainties. The superiority of the proposed controller over the proportional integral derivative (PID) controller that usually being used in a low-cost robotic arm development is confirmed that it has better trajectory tracking. The error tracking along the slope of sinusoidal trajectory input was suppressed to zero. The biggest error along the trajectory that happened on every peak of the sinusoidal input, or when the direction is changed has been improved from 15 degrees to 4 degrees. This can be conceived that the proposed controller can be applied to control a low-cost robotic arm joint position which is applicable for small industries or educational purpose.
Spoken language identification using i-vectors, x-vectors, PLDA and logistic regression Ahmad Iqbal Abdurrahman; Amalia Zahra
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.2893

Abstract

In this paper, i-vector and x-vector is used to extract the features from speech signal from local Indonesia languages, namely Javanese, Sundanese and Minang languages to help classifier identify the language spoken by the speaker. Probabilistic linear discriminant analysis (PLDA) are used as the baseline classifier and logistic regression technique are used because of prior studies showing logistic regression has better performance than PLDA for classifying speech data. Once these features are extracted. The feature is going to be classified using the classifier mentioned before. In the experiment, we tried to segment the test data to three segment such as 3, 10, and 30 seconds. This study is expanded by testing multiple parameters on the i-vector and x-vector method then comparing PLDA and logistic regression performance as its classifier. The x-vector has better score than i-vector for every segmented data while using PLDA as its classifier, except where the i-vector and x-vector is using logistic regression, i-vector still has better accuracy compared to x-vector.
Expecting confirmed and death cases of covid-19 in Iraq by utilizing backpropagation neural network Moatasem Yaseen Al-Ridha; Ammar Sameer Anaz; Raid Rafi Omar Al-Nima
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.2876

Abstract

The world is currently facing a strong epidemic and pandemic of coronavirus. This motivates establishing our paper, where this virus pushes researchers to study, investigate, observe, analyse and try solving its related issues. In this work, an artificial neural network (ANN) model of backpropagation neural network (BNN) with two hidden layers is proposed for expecting confirmed cases and death cases of coronavirus disease 2019 (covid-19). As a field of study, Iraq country has been considered in this paper. Covid-19 dataset from our world in data (OWID) is used here. Promising result is achieved where a very small error value of 0.0035 is reported in overall the evaluations. This paper may implicate establishing further researches that consider other parameters and other countries over the world. It is worth mentioning that the suggested ANN model may help decision maker people in taking quarantine movements against the strong epidemic and pandemic of covid-19.
Simulation analysis of equivalent circuit model of skin-electrode impedance for transcutaneous electrical stimulation Joni Welman Simatupang; Wilbert Wijaya; David Tyler; Clementine Mavridis
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.3080

Abstract

For more than 50 years, transcutaneous electrical stimulation method has been used to cure the spinal cord injury, stroke or cerebral palsy. This method works by activating the excitable nerves, muscle fibers by electrical current stimulation through electrode to skin interface. Electrode to skin interface requires equivalent circuit to overcome the inability of measuring the skin resistivity directly. We have learned several previous models, which are from Lawler, Moineau and Keller and Kuhn. Unfortunately, Moineau model neglects the capacitance effect, while Lawler and Keller and Kuhn include capacitive and resistive nature of skin in their equivalent circuits. Both models consisted of only one parallel RC block. Therefore, this paper presents the simulation results of the proposed equivalent circuit model using two parallel RC circuits. Simulation of the proposed model is conducted in MATLAB 2015a and compared with two previous models using certain parameters. Results show that the proposed model obtained the impedance of 10.830 kΩ when it is simulated using 100Hz frequency, for Lawler model the impedance is 5.340 kΩ and Keller and Kuhn model the impedance obtained is 6.490 kΩ. The proposed model has the refined impedance compared with other models and is expected to deliver better electrical stimulation.
Design compact microstrap patch antenna with T-shaped 5G application Ali Abdulateef Abdulbari; Mustafa Mohammed Jawad; H. O. Hanoosh; Murtaja Ali Saare; Saima Anwar Lashari; Sari Ali Sari; Sarosh Ahmad; Yaser Khalill; Yaqdhan Mahmood Hussain
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.2935

Abstract

This paper is presents a microstrap patch with a T-shaped rectangular antenna workings; the T-shaped patch operating at 3.6 GHz resonating frequency range for 5G application (from 2.9 to 4.4 GHz) repectively. The overall size of the proposed antenna is 22×24×0.25 mm3; the feeding technique using a 50 Ω feed line to the antenna. The proposed antenna is printed on compact Rogers RT 588 lz substrate having permittivity (ɛr) 2.00, loss tangent (tan δ) 0.0021, with thikness 0.2 mm. The proposed antenna introducesmany advantages like small size, low profile, and simpler structure. The characteristics such as radiation pattern, reflection coefficient, gain, current distribution, and radiation efficiency are respectively presented and discussed, using CST microwave study in simulating and analysing. Introducing a slot with a rectangular T-shaped patch antenna achieved lower frequency with 98.474% radiation efficiency and peak gain of the proposed antenna at 2.52 dB. The fractional bandwidth is 42.81% (2.90 GHz to 4.48 GHz) with a resonant frequency of 3.6 GHz and return loss at 28.76 dB. This frequency band attributessuited 5 G mobile application.
Keyseg: adaptive segmentation for spontaneous electroencephalography map series into spatially defined microstates of musicians’ brain Indra K. Wardani; Phakkharawat Sittiprapaporn; Djohan Djohan; Fortunata Tyasrinestu; Prayoon Suyajai
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.3063

Abstract

Music is being studied related to either its impact on the psychological interaction or cognitive process behind it. These examinations bring out music's coordination to numerous disciplines including neuroscience. A few past examinations exhibited the contrast among musicians and non-musicians regarding brain structure and brain activity. The current investigation exhibited the diverse brain activation while musicians tuned in to music with regards to their musical experiences utilizing microstate classes method analysis. The investigation intended to determine electroencephalography microstate changes in Karawitan musicians' brain while tuning in to Gendhing Lancaran. Applying the electroencephalography microstate investigation of Karawitan musicians, the occurrence parameters was computed for four microstate classes (A, B, C, and D). Microstate properties were compared among subjects and correlated to Gendhing Lancaran perception. The present results revealed that Karawitan musicians' brain were characterized by microstate classes with the increased prominence of classes A, B, and D, but decreased prominence of classes C while tuning in to Gendhing Lancaran. Our finding is the first study to identify the typical microstate characteristics of the Karawitan musician’s brains while tuning in to Gendhing Lancaran by using the microstate segmentaion method.