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Bulletin of Electrical Engineering and Informatics

EEI
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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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A Nonlinear TSNN Based Model of a Lead Acid Battery El Mehdi Laadissi; Anas El Filali; Malika Zazi
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

The paper studies a nonlinear model based on time series neural network system (TSNN) to improve the highly nonlinear dynamic model of an automotive lead acid cell battery. Artificial neural network (ANN) take into consideration the dynamic behavior of both input-output variables of the battery charge-discharge processes. The ANN works as a benchmark, its inputs include delays and charging/discharging current values. To train our neural network, we performed a pulse discharge on a lead acid battery to collect experimental data. Results are presented and compared with a nonlinear Hammerstein-Wiener model. The ANN and nonlinear autoregressive exogenous model (NARX) models achieved satisfying results.

Improving Classification Accuracy Using Clustering Technique Norsyela Muhammad Noor Mathivanan; Nor Azura Md.Ghani; Roziah Mohd Janor
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Product classification is the key issue in e-commerce domains. Many products are released to the market rapidly and to select the correct category in taxonomy for each product has become a challenging task. The application of classification model is useful to precisely classify the products. The study proposed a method to apply clustering prior to classification. This study has used a large-scale real-world data set to identify the efficiency of clustering technique to improve the classification model. The conventional text classification procedures are used in the study such as preprocessing, feature extraction and feature selection before applying the clustering technique. Results show that clustering technique improves the accuracy of the classification model. The best classification model for all three approaches which are classification model only, classification with hierarchical clustering and classification with K-means clustering is K-Nearest Neighbor (KNN) model. Even though the accuracy of the KNN models are the same across different approaches but the KNN model with K-means clustering had the shortest time of execution. Hence, applying K-means clustering prior to KNN model helps in reducing the computation time.

An Enhanced FPGA Based Asynchronous Microprocessor Design Using VIVADO and ISIM Archana Rani; Naresh Grover
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

This paper deals with the novel design and implementation of asynchronous microprocessor by using HDL on Vivado tool wherein it has the capability of handling even I-Type, R-Type and Jump instructions with multiplier instruction packet. Moreover, it uses separate memory for instructions and data read-write that can be changed at any time. The complete design has been synthesized and simulated using Vivado. The complete design is targeted on Xilinx Virtex-7 FPGA. This paper more focuses on the use of Vivado Tool for advanced FPGA device. By using Vivado we get enhaced analysis result for better view of properly Route and Placed design.

Augmented reality: effect on conceptual change of scientific Danakorn Nincarean Eh Phon; Ahmad Firdaus Zainal Abidin; Mohd Faizal Ab Razak; Shahreen Kasim; Ahmad Hoirul Basori; Tole Sutikno
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

In recent years, Augmented Reality (AR) has received increasing emphasis and researchers gradually promote it Over the worlds. With the unique abilities to generate virtual objects over the real-world environment, it can enhance user perception. Although AR recognised for their enormous positive impacts, there are still a ton of matters waiting to be discovered. Research studies on AR toward conceptual change, specifically in scientific concept, are particularly limited. Therefore, this research aims to investigate the effect of integrating AR on conceptual change in scientific concepts. Thirty-four primary school students participated in the study. A pre-test and post-test were used to assess participants’ understanding of the scientific concepts before and after learning through AR. The findings demonstrated that 82% among them had misconceptions about the scientific concepts before learning through AR. However, most of them (around 88%) able to correct their misconceptions and shifted to have a scientific conceptual understanding after learning through AR. These findings indicate that AR was effective to be integrated into education to facilitate conceptual change.

Adaboost-multilayer perceptron to predict the student’s performance in software engineering Ahmad Firdaus Zainal Abidin; Mohd Faaizie Darmawan; Mohd Zamri Osman; Shahid Anwar; Shahreen Kasim; Arda Yunianta; Tole Sutikno
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Software Engineering (SE) course is one of the backbones of today's computer technology sophistication. Effective theoretical and practical learning of this course is essential to computer students. However, there are many students fail in this course. There are many aspects that influence a student's performance. Currently, student performance analysis methods just focus on historical achievement and assessment methods given in the class. Need more research to predict student's performance to overcome the problem of student failing. The objective of this research is to perform a prediction for student's performance in the SE using enhanced Multilayer Perceptron (MLP) machine learning classification with Adaboost. This research also investigates the requirements of each student before registering in this course. This research achieved 87.76 percent accuracy in classifying the performance of SE students.

Review of the machine learning methods in the classification of phishing attack John Arthur Jupin; Tole Sutikno; Mohd Arfian Ismail; Mohd Saberi Mohamad; Shahreen Kasim; Deris Stiawan
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

The development of computer networks today has increased rapidly. This can be seen based on the trend of computer users around the world, whereby they need to connect their computer to the Internet. This shows that the use of Internet networks is very important, whether for work purposes or access to social media accounts. However, in widely using this computer network, the privacy of computer users is in danger, especially for computer users who do not install security systems in their computer. This problem will allow hackers to hack and commit network attacks. This is very dangerous, especially for Internet users because hackers can steal confidential information such as bank login account or social media login account. The attacks that can be made include phishing attacks. The goal of this study is to review the types of phishing attacks and current methods used in preventing them. Based on the literature, the machine learning method is widely used to prevent phishing attacks. There are several algorithms that can be used in the machine learning method to prevent these attacks. This study focused on an algorithm that was thoroughly made and the methods in implementing this algorithm are discussed in detail.

Application of phase-metric compensation method for geoelectric control of near-surface geodynamic processes Kuzichkin O. R.; Vasilyev G. S.; Grecheneva A. V.; Mikhaleva E. V.; Baknin M. D.; Surzhik D. I.
Bulletin of Electrical Engineering and Informatics Vol 9, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

This work is devoted to the application of the compensation method of geoelectric control using the field phase characteristics for the detection and localization of geodynamic processes on example of the development of the karst-suffusion cavity. High noise immunity of the phase-metric registration method of geoelectric signals in comparison with amplitude parameters of the anomalous components of electromagnetic field, usually used to analyze the results of observations, is noted. A formalized approach to the use of phase characteristics of the field for the interpretation of monitoring data and associated problem of geodynamic processes localization is developed. In the framework of this approach, the section parameters are proposed to determine by the minimum sum of weighted mean square interpretation error and regularizing functional containing a priori information about the geoelectric section. To check localization possibility of the spherical karst-suffusion cavity, simulation of the amplitude and phase anomalous components of the field potential, as well as the standard error of heterogeneity localization, when moving the sphere center along the installation profile, is carried out. Simulation has shown a good potential differentiation from the inhomogeneity position, the highest accuracy of localization is achieved with combined use of amplitude and phase field components in the problem of inhomogeneity localization.

Information processing in info-communication media of geodynamic monitoring on the basis of adaptive queuing system Kuzichkin O. R.; Eremenko V. T.; Loginov I. V.; Grecheneva A. V.; Eremenko A. V.; Dorofeev N. V.
Bulletin of Electrical Engineering and Informatics Vol 9, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

The paper considers the problem of modeling the processing of geodynamic information in a reconfigurable information-communication environment of geodynamic monitoring based on an adaptive queuing system. It was established that due to the complexity and diversity of geodynamic information, the number of geodynamic control points is expanding and the number of controlled parameters, the number of controlled objects of natural-technical systems (NTS) are increasing. This leads to an increase in the overall load on the info-communication monitoring environment, an increase in the processing time and the response time of the NTS geodynamic stability control system to negative changes. This leads to the need for adaptive optimization of the info-communication environment of geodynamic monitoring. The model of a composite stream describing the change in the nature of the tasks performed by geodynamic monitoring was determined, approaches to reconfiguration management based on minimization of the loss functional were proposed. The constructed model of the process of functioning and the analytical dependencies obtained for it allow analyzing the process of processing geodynamic information in info-communication environments to optimize the mechanisms of their functioning.

Determination of transient thermal characteristics for thermal electric behavioral models of integrated circuits Drakin A. Yu; Potapov L. A.; Shkolin A. N.
Bulletin of Electrical Engineering and Informatics Vol 9, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

In the current study, it was tried to describe a method for determining thermal characteristics of integrated micro-circuits to identify thermal parameters of multidisciplinary (thermal-electric) behavioral models. The problem is solved on the example of high-frequency pulse voltage converters. A solution was proposed to refine the minimum structure of the thermoelectric model based on an iterative least squares method using the Levenberg-Marquardt algorithm, as well as a graph of the spectral den-sity of time constants. This made it possible to reduce the influence of the filtering factor in the deconvolution operation when building a thermal model using the structural function of the thermal characteristic transition. Also, the results obtained can be used to build integrated circuits (IC) behavioral models, taking into account the thermal processes occurring in them.

Application of deep learning to enhance the accuracy of intrusion detection in modern computer networks Jafar Majidpour; Hiwa Hasanzadeh
Bulletin of Electrical Engineering and Informatics Vol 9, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Application of deep learning to enhance the accuracy of intrusion detection in modern computer networks were studied in this paper. The identification of attacks in computer networks is divided in to two categories of intrusion detection and anomaly detection in terms of the information used in the learning phase. Intrusion detection uses both routine traffic and attack traffic. Abnormal detection methods attempt to model the normal behavior of the system, and any incident that violates this model is considered to be a suspicious behavior. For example, if the web server, which is usually passive, tries to There are many addresses that are likely to be infected with the worm. The abnormal diagnostic methods are Statistical models, Secure system approach, Review protocol, Check files, Create White list, Neural Networks, Genetic Algorithm, Vector Machines, decision tree. Our results have demonstrated that our approach offers high levels of accuracy, precision and recall together with reduced training time. In our future work, the first avenue of exploration for improvement will be to assess and extend the capability of our model to handle zero-day attacks.

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