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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 63 Documents
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Search results for , issue "Vol 10, No 5: October 2021" : 63 Documents clear
Detection of acute stress caused by cognitive tasks based on physiological signals Valentina Markova; Todor Ganchev; Kalin Kalinkov; Miroslav Markov
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3130

Abstract

We report on the development of an automated detector of acute stress based on physiological signals. Our detector discriminates between high and low levels of acute stress accumulated by students when performing cognitive tasks on a computer. The proposed detector builds on well-known physiological signal processing principles combined with the state-of-art support vector machine (SVM) classifier. The novelty aspects here come from the design and implementation of the signal pre-processing and the feature extraction stages, which were purposely designed and fine-tuned for the specific needs of acute stress detection and from applying existing algorithms to a new problem. The proposed acute stress detector was evaluated in person-specific and person-independent experimental setups using the publicly available CLAS dataset. Each setup involved three cognitive tasks with a dissimilar crux of the matter and different complexity. The experimental results indicated a very high detection accuracy when discriminating between acute stress conditions due to significant cognitive load and conditions elicited by two typical emotion elicitation tasks. Such a functionality would also contribute towards obtaining a multi-faceted analysis on the dependence of work efficiency from personal treats, cognitive load and acute stress level.
Application of PI controller based active filter for harmonic mitigation of grid-connected PV-system Achala Khandelwal; Pragya Nema
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.2907

Abstract

The recent trends show the interconnection of PV system with electric grid. With this configuration the issue of harmonics comes into existence. The mounting figure of power-electronic instruments has formed considerable impression on the power-quality of electric supply. Harmonics deformations have conventionally been handled amid the application of passive-LC filters. Active Filter has emerged as a good substitute for passive filters to reduce the harmonics to great extent as it has numerous benefits over the former filters. The active filter’s most vital part is the applied control strategies. Several researches are being under process to advance the functioning of the filter. One of the important control requirements of filter is the regulation of DC link up capacitor voltage. Here the voltage supervision of capacitor is being done using PI controller. The paper show current harmonics compensation of PV grid connected system using PI controller based active filter. Simulation outcomes have been shown which displays the harmonics are within the IEEE boundaries.
Improvement of double-layer phosphor structure WLEDS in color homogeneity and luminous flux Dieu An Nguyen Thi; Phung Ton That; Hoang Nam Nguyen
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.2914

Abstract

The concept of the analysis is to put a CaAl2O4:Mn2+ green phosphor layer on top of the YAG:Ce3+ yellow phosphor layer. After that, find the added CaAl2O4:Mn2+ concentration appropriate for the highest luminous flux (LF) and color homogeneity (CH). In this analysis, five equivalent WLEDs were applied but with distinct color temperatures, including 5600 K - 8500 K. The findings showed that CaAl2O4:Mn2+ brings great benefits to increase not only the luminous flux but also the color homogeneity. Especially, the higher the CaAl2O4:Mn2+ concentration, the more the luminous flux released by WLEDs, owing to the risen content of the light of green in WLEDs. Nevertheless, as the CaAl2O4:Mn2+ concentration raised significantly, a small reduction in the color rendering metric (CRI) and color quality scale (CQS) occurred. This is supported by simulation and calculation according to the theory of Monte Carlo. The paper results are the crucial contribution to the manufacture of WLEDs with better optical performance and color homogeneity of remote phosphor configurations.
DCT based feature extraction and support vector machine classification for musical instruments tone recognition Linggo Sumarno; Rifai Chai
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3158

Abstract

The conducted research proposes a feature extraction and classification combination method that is used in a tone recognition system for musical instruments. It is expected that by implementing this combination, the tone recognition system will require fewer feature extraction coefficients than those previously investigated. The proposed combination comprises of feature extraction using discrete cosine transform (DCT) and classification using support vector machine (SVM). Bellyra, clarinet, and pianica tones were used in the experiment, with each indicating a tone with one, several, or many major local peaks in the transform domain. Based on the results of the tests, the proposed combination is efficient enough to be used in a tone recognition system for musical instruments. This is indicated in recognizing a tone, it only needs at least eight feature extraction coefficients.
Investigation on the ohmic characteristic of Ni/Ti/4H-SiC M. I. Idris; Z. A. F. M. Napiah; Marzaini Rashid; M. N. Shah Zainudin; Siti Amaniah Mohd Chachuli; M. A. Azam
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3174

Abstract

Ohmic contact is important for silicon carbide (SiC) devices such as Schottky diode, junction field effect transistor (JFET) and metal oxide transistor (MOSFET). The effect of post metallization annealing (PMA) on the ohmic characteristics of Ni/Ti/4H-SiC is investigated. The samples were annealed under different ambients of high vacuum, forming gas and N2 gas at 1050˚C for 3 minutes using rapid thermal process (RTP). Current-voltage (I-V) measurements taken for different distances of a transmission line model (TLM) structure have been utilized to extract the contact resistivity. The correlation between surface roughness and resistivity has been investigated. It was found that the involvement of nitrogen during the annealing process at 1050˚C was ineffective to reduce the contact resistivity. The resistivity is improved when the samples were annealed in forming gas (FG), (a mixture of H2+N2) environment, showing that the incorporation of H2 gas during the annealing process has produced a better result. On the other hand, high vacuum PMA was found to be effective to improve the ohmic characteristic with higher current level at lower voltage. Hence, the enhanced performance observed in high vacuum annealing samples is beneficial to get ohmic contact on Ni/Ti/4H-SiC for PMA process with a low thermal budget.
Freshness assessment of tilapia fish in traditional market based on an electronic nose Radi Radi; Eka Wahyudi; Muhammad Danu Adhityamurti; Joko Purwo Leksono Yuroto Putro; Barokah Barokah; Dwi Noor Rohmah
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3111

Abstract

This study evaluates an e-nose based on gas sensors to measure the freshness of tilapia. The device consists of a series of semiconductor sensors as detector, a combination of valve-vial-oxygen as sample delivery system, a microcontroller as interface and controller, and a computer for data recording and processing. The e-nose was firstly used to classify the fresh and non-fresh tilapia. A total of 48 samples of fresh tilapia and 50 samples of non-fresh tilapia were prepared and measured using the e-nose through three stages, namely: flushing, collecting, and purging. The sensor responses were processed into aroma patterns, then classified by two pattern classification softwares of principal component analysis (PCA) and neural network (NN). There were four methods for aroma patterns formation being evaluated: absolute data, normalized absolute data, relative data, normalized relative data. The results showed that the normalized absolute data method provides the best classification with the accuracy level of 93.88%. With this method, the trained NN was used to predict the freshness of 15 tilapia samples collected from a traditional market. The result showed that 60.0% of the samples are classified into fresh category, 33.3% are in the non-fresh category, and 6.7% are not included in both categories.
Comparative analysis of the essential CPU scheduling algorithms Hoger K. Omar; Kamal H. Jihad; Shalau F. Hussein
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.2812

Abstract

CPU scheduling algorithms have a significant function in multiprogramming operating systems. When the CPU scheduling is effective a high rate of computation could be done correctly and also the system will maintain in a stable state. As well as, CPU scheduling algorithms are the main service in the operating systems that fulfill the maximum utilization of the CPU. This paper aims to compare the characteristics of the CPU scheduling algorithms towards which one is the best algorithm for gaining a higher CPU utilization. The comparison has been done between ten scheduling algorithms with presenting different parameters, such as performance, algorithm’s complexity, algorithm’s problem, average waiting times, algorithm’s advantages-disadvantages, allocation way, etc. The main purpose of the article is to analyze the CPU scheduler in such a way that suits the scheduling goals. However, knowing the algorithm type which is most suitable for a particular situation by showing its full properties.
Computer model for tsunami vulnerability using sentinel 2A and SRTM images optimized by machine learning Sri Yulianto Joko Prasetyo; Bistok Hasiholan Simanjuntak; Kristoko Dwi Hartomo; Wiwin Sulistyo
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3100

Abstract

This study aims to develop a software framework for modeling of tsunami vulnerability using DEM and Sentinel 2 images. The stages of study, are: 1) extraction Sentinel 2 images using algorithms NDVI, NDBI, NDWI, MSAVI, and MNDWI; 2) prediction vegetation indices using machine learning algorithms. 3) accuracy testing using the MSE, ME, RMSE, MAE, MPE, and MAPE; 4) spatial prediction using Kriging function and 5) modeling tsunami vulnerability indicators. The results show that in 2021 the area was dominated by vegetation density between (-0.1-0.3) with moderate to high vulnerability and risk of land use tsunami as a result of the decreasing of vegetation. The prediction results for 2021 show a low canopy density of vegetation and a high degree of land surface slope. Based on the prediction results in 2021, the study area mostly shows the existence of built-up lands with a high tsunami vulnerability risk (more than 0.1). Vegetation population had decreased to 67% from the original areas in 2017 with an area of 135 km2. Forest vegetation had decreased by 45% from 116 km2 in 2017. Land use for fisheries had increased to the area of 86 km2 from 2017 with an area of 24 km2.
An implementation of real-time detection of cross-site scripting attacks on cloud-based web applications using deep learning Isaac Odun- Ayo; Williams Toro- Abasi; Marion Adebiyi; Oladapo Alagbe
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3168

Abstract

Cross-site scripting has caused considerable harm to the economy and individual privacy. Deep learning consists of three primary learning approaches, and it is made up of numerous strata of artificial neural networks. Triggering functions that can be used for the production of non-linear outputs are contained within each layer. This study proposes a secure framework that can be used to achieve real-time detection and prevention of cross-site scripting attacks in cloud-based web applications, using deep learning, with a high level of accuracy. This project work utilized five phases cross-site scripting payloads and Benign user inputs extraction, feature engineering, generation of datasets, deep learning modeling, and classification filter for Malicious cross-site scripting queries. A web application was then developed with the deep learning model embedded on the backend and hosted on the cloud. In this work, a model was developed to detect cross-site scripting attacks using multi-layer perceptron deep learning model, after a comparative analysis of its performance in contrast to three other deep learning models deep belief network, ensemble, and long short-term memory. A multi-layer perceptron based performance evaluation of the proposed model obtained an accuracy of 99.47%, which shows a high level of accuracy in detecting cross-site scripting attacks.
Twitter sentimental analysis from time series facts: the implementation of enhanced support vector machine Abhishek Kumar; Vishal Dutt; Vicente García-Díaz; Sushil Kumar Narang
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3078

Abstract

Sentiment analysis through textual data mining is an indispensable system used to extract the contextual social information from the texts submitted by the intended users. Now days, world wide web is playing a vital source of textual content being shared in different communities by the people sharing their own sentiments through the websites or web blogs. Sentiment analysis has become a vital field of study since based on the extracted expressions, individuals or the businesses can access or update their reviews and take significant decisions. Sentimental mining is typically used to classify these reviews depending on its assessment as whether these reviews come out to be neutral, positive or negative. In our study, we have boosted feature selection technique with strong feature normalization for classifying the sentiments into negative, positive or neutral. Afterwards, support vector machine (SVM) classifier powered with radial basis kernel with adjusted hyper plane parameters, was employed to categorize reviews. Grid search with cross validation as well as logarithmic scale were employed for optimal values of hyper parameters. The classification results of this proposed system provides optimal results when compared to other state of art classification methods.

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