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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 66 Documents
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Search results for , issue "Vol 24, No 1: October 2021" : 66 Documents clear
Design of dual band slotted reconfigurable antenna using electronic switching circuit Mustafa M. Al-Saeedi; Ahmed A. Hashim; Omer Al-Bayati; Ali Salim Rasheed; Rasool Hasan Finjan
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp386-393

Abstract

This paper proposes a dual band reconfigurable microstrip slotted antenna for supporting the wireless local area network (WLAN) and worldwide interoperability for microwave access (WiMAX) applications, providing coverage where both directive and omni-directive radiations are needed. The design consists of a feedline, a ground plane with two slots and two gaps between them to provide the switching capability and a 1.6 mm thick flame retardant 4 (FR4) substrate (dielectric constant Ɛ=4.3, loss tangent δ=0.019), modeling an antenna size of 30x35x1.6 mm3. The EM simulation, which was carried out using the connected speech test (CST) studio suite 2017, generated dual wide bands of 40% (2-3 GHz) with -55 dB of S11 and 24% (5.2-6.6 GHz) higher than its predecessors with lower complexity and -60 dB of S11 in addition to the radiation pattern versatility while maintaining lower power consumption. Moreover, the antenna produced omnidirectional radiation patterns with over than 40% bandwith at 2.4 GHz and directional radiation patterns with 24% bandwith at the 5.8 GHz band. Furthermore, a comprehensive review of previously proposed designs has also been made and compared with current work.
Decision support system on quality assessment of the prospective civil servant’s education and training using fuzzy method Aris Susanto; Omar Wahid; Hazriani Hazriani; Yuyun Yuyun
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp519-529

Abstract

This study aims to develop a decision support system using the fuzzy method in order to assess the quality of education and training of prospective civil servants and highlight possible improvement considerations. The assessment consists of six criterias, namely coaches, lecturers, preachers, mentors, examiners, and administrators. Based on the evaluation result of the quality level of each criterion, it is obtained that the top two criterion are examiners and preachers, followed by coaches, lecturers, advisors, and the lowest is organizer. In addition, the quality of the civil servant class III training is better than the class II civil servant training. It also shows that the value of the organizers criterion has different level of satisfactions. Overall, the quality of the training (according to the participants' opinion) was very good with a score of 92.50 for training class II and 95.20 for training class III. Furthermore, it is necessary to conduct research to determine the quality of the training each year by looking at the achievements of the participants. The >system testing obtained an accuracy of 100%, whichs implies that the system can be used to assess the quality of education and training appropriately.
The effect of automated swab robot: new technology drives new behavior Jonalyn Mae E. Aranda; Jasper Rae Zeus A. Antonio
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp99-107

Abstract

The world is now faced with a devastating pandemic outbreak coronavirus disease-2019 (COVID-19). The latest coronavirus infected almost all continents and witnessed sharp rises in cases diagnosed. The engineers tend to eliminate the matter and have solutions, one in every utilizing technical innovation. Researchers from Singapore, Taiwan, and Denmark have developed a fully automated robot that may take coronavirus swabs in order for health care professionals don’t seem to be exposed to the chance of infection. The objective of this study is to present the potential effects of robotics to help healthcare professionals on getting specimens and testing for COVID-19. These possible consequences include positive and negative outcomes and as a result, the overall impact on the profit or loss to society is far from obvious. The paper discusses two theoretical scenarios, distinguished fundamentally by the different behavioral responses of the automated swab robot and the selection of results in line with policy interventions.
Evolutionary approach to secure mobile telecommunication networks Abdelkader Ghazli; Adda Alipacha; Naima Hadj Said
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp357-366

Abstract

A series of encryption algorithms called A5 is used to secure mobile telephone communications, producing a pseudo-random sequence that will be exclusive OR (XORed) with the data flowing in the air interface in order to secure them. These algorithms are essentially composed of shift registers with linear feedback, controlled generally by a function or with another register in order to favor the randomness character of the keystream generated. Evolutionary algorithms are bioinspired calculation methods, whose principle is inspired by the theory of evolution, which consists in evolving a set of solutions to a problem given in order to find better results. This paper presents an improvement of the A5/1 algorithm by an evolutionary approach based on the use of particle swarm optimization algorithm (PSO) in order to limit some weaknesses and drawbacks found in the conventional A5/1 version, which have been cryptanalysed and several attacks have been published such as time memory trade off attacks and guess and determine attacks. Our technique does not alter the A5/1's architecture, but it does help to improve its shifting system by an evolutionary approach, which guarantees the quality of the keystream generated and makes it more complex and more secure.
Static hand gesture recognition of Arabic sign language by using deep CNNs Mohammad H. Ismail; Shefa A. Dawwd; Fakhradeen H. Ali
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp178-188

Abstract

An Arabic sign language recognition using two concatenated deep convolution neural network models DenseNet121 & VGG16 is presented. The pre-trained models are fed with images, and then the system can automatically recognize the Arabic sign language. To evaluate the performance of concatenated two models in the Arabic sign language recognition, the red-green-blue (RGB) images for various static signs are collected in a dataset. The dataset comprises 220,000 images for 44 categories: 32 letters, 11 numbers (0:10), and 1 for none. For each of the static signs, there are 5000 images collected from different volunteers. The pre-trained models were used and trained on prepared Arabic sign language data. These models were used after some modification. Also, an attempt has been made to adopt two models from the previously trained models, where they are trained in parallel deep feature extractions. Then they are combined and prepared for the classification stage. The results demonstrate the comparison between the performance of the single model and multi-model. It appears that most of the multi-model is better in feature extraction and classification than the single models. And also show that when depending on the total number of incorrect recognize sign image in training, validation and testing dataset, the best convolutional neural networks (CNN) model in feature extraction and classification Arabic sign language is the DenseNet121 for a single model using and DenseNet121 & VGG16 for multi-model using.
Improved fingerprinting performance in indoor positioning by reducing duration of the training phase process Andika Muharam; Abdi Wahab; Mudrik Alaydrus
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp236-244

Abstract

Wireless sensor network (WSN) can be used as a solution to find out the position of an object that cannot be reached by global positioning system (GPS), for example to find out the position of objects in a room known as Indoor Positioning. One method in indoor positioning that can be used is fingerprinting. Inside there are two main work phases, namely training and positioning. The training phase is the process of collecting received signal strength indication (RSSI) data levels from each sensor Node reference that will be used as a reference value for the positioning phase. The more sensor Nodes used, the longer the processing time needed in the training phase. This research focussed on the duration of the training phase, the implementation of which are used 4 sensor Nodes, namely Zigbee (IEEE 802.15.4 protocol) arranged according to mesh network topology, one as Node X (positioning target) and 3 as reference Nodes. There are two methods used in the training phase, namely fixed target parameter (FTP) and moving target parameter (MTP). MTP took 5 seconds faster than FTP in terms of the duration of RSSI data collection from each reference Node. 
Integrated data aggregation with fault-tolerance and lifetime energy-aware adaptive routing in coffee plantations using WSNs Roshan Zameer Ahmed; Sravani K.; Shilpa S. Chaudhari; S. Sethu Selvi; S. L. Gangadharaiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp376-385

Abstract

The pest namely coffee white stem borer (CWSB) has harmed the economic progress of many emerging countries as a result of arabica coffee’s agricultural products. The boring activity causes the stem to shrink, fade in color, and acquire translucent margins across the stem. The pest multiplier can be controlled by capturing the location with the utilization of a wireless sensor networks (WSNs) and blocking its exit point at the user end. In this work, we propose an integrated data aggregation with faulttolerance and lifetime energy-aware adaptive routing (IDALAR) approach to transfer the sensed pest location data. The efficient packet format and statistical models based routing between clusterheads (CHs) and base station (BS) is proposed considering the availability of resources such as message overhead, algorithmic complexity, residual energy, and control overhead are all used to calculate its performance.
Optimized machine learning algorithm for intrusion detection Royida A. Ibrahem Alhayali; Mohammad Aljanabi; Ahmed Hussein Ali; Mostafa Abdulghfoor Mohammed; Tole Sutikno
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp590-599

Abstract

Intrusion detection is mainly achieved by using optimization algorithms. The need for optimization algorithms for intrusion detection is necessitated by the increasing number of features in audit data, as well as the performance failure of the human-based smart intrusion detection system (IDS) in terms of their prolonged training time and classification accuracy. This article presents an improved intrusion detection technique for binary classification. The proposal is a combination of different optimizers, including Rao optimization algorithm, extreme learning machine (ELM), support vector machine (SVM), and logistic regression (LR) (for feature selection & weighting), as well as a hybrid Rao-SVM algorithm with supervised machine learning (ML) techniques for feature subset selection (FSS). The process of selecting the least number of features without sacrificing the FSS accuracy was considered a multi-objective optimization problem. The algorithm-specific, parameter-less concept of the proposed Rao-SVM was also explored in this study. The KDDCup 99 and CICIDS 2017 were used as the intrusion dataset for the experiments, where significant improvements were noted with the new Rao-SVM compared to the other algorithms. Rao-SVM presented better results than many existing works by reaching 100% accuracy for KDDCup 99 dataset and 97% for CICIDS dataset.
Identification of user’s credibility on twitter social networks Faraz Ahmad; S. A. M. Rizvi
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp554-563

Abstract

Twitter is one of the most influential social media platforms, facilitates the spreading of information in the form of text, images, and videos. However, the credibility of posted content is still trailed by an interrogation mark. Introduction: In this paper, a model has been developed for finding the user’s credibility based on the tweets which they had posted on Twitter social networks. The model consists of machine learning algorithms that assist not only in categorizing the tweets into credibility classes but also helps in finding user’s credibility ratings on the social media platform. Methods and results: The dataset and associated features of 100,000 tweets were extracted and pre-processed. Furthermore, the credibility class labelling of tweets was performed using four different human annotators. The meaning cloud and natural language understanding platforms were used for calculating the polarity, sentiment, and emotions score. The K-Means algorithm was applied for finding the clusters of tweets based on features set, whereas, random forest, support vector machine, naïve Bayes, K-nearest-neighbours (KNN), J48 decision tree, and multilayer perceptron were used for classifying the tweets into credibility classes. A significant level of accuracy, precision, and recall was provided by all the classifiers for all the given credibility classes.
Forgery detection algorithm based on texture features Ismail Taha Ahmed; Baraa Tareq Hammad; Norziana Jamil
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp226-235

Abstract

Any researcher's goal is to improve detection accuracy with a limited feature vector dimension. Therefore, in this paper, we attempt to find and discover the best types of texture features and classifiers that are appropriate for the coarse mesh finite differenc (CMFD). Segmentation-based fractal texture analysis (SFTA), local binary pattern (LBP), and Haralick are the texture features that have been chosen. K-nearest neighbors (KNN), naïve Bayes, and Logistics are also among the classifiers chosen. SFTA, local binary pattern (LBP), and Haralick feature vector are fed to the KNN, naïve Bayes, and logistics classifier. The outcomes of the experiment indicate that the SFTA texture feature surpassed all other texture features in all classifiers, making it the best texture feature to use in forgery detection. Haralick feature has the second-best texture feature performance in all of the classifiers. The performance using the LBP feature is lower than that of the other texture features. It also shows that the KNN classifier outperformed the other two in terms of accuracy. However, among the classifiers, the logistic classifier had the lowest accuracy. The proposed SFTA based KNN method is compared to other state-of-the-art techniques in terms of feature dimension and detection accuracy. The proposed method outperforms other current techniques.

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