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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 9,174 Documents
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Detection and extraction of digital footprints from the iDrive cloud storage using web browser forensics analysis Adesoji Adesina; Ayodele Adebiyi; Charles Ayo
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp550-559

Abstract

STorage as a service (STaaS) allows its subscribers the ability to access their stored data with the use of internet enabled digital devices at anywhere, anyplace and anytime. The easy accessibility of cloud storage with digital devices is one of the major benefits of cloud computing but this benefit can also be exploited by cybercriminals to perform various forms of malicious usages. During forensic investigation, forensic examiners are expected to provided evidence in relation to the malicious usages but the physical inaccessibility to the digital artifacts on the cloud servers, the difficulty in retrieving evidential artifacts from various cloud storage services and the difficulty in obtaining forensic logs from the concerned cloud service providers among other factors make it difficult to perform forensic investigations. This paper provided step by step experimental guidelines to extract digital artifacts from Google Chrome and Internet Explorer from Windows 10 personal computer using iDrive cloud storage as a case study. The study used Nirsoft forensic tool to locate the relevant forensic artifacts and an integrated conceptual digital forensic framework was adopted to carry out the investigation. This study increases the knowledge of client forensics using web browser analysis during cloud storage forensic investigation.
Adaptive filter algorithms for state of charge estimation methods: A comprehensive review Shamsul Aizam Zulkifli; Mubashir Hayat Khan
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1360-1367

Abstract

Battery management system is compulsory for long life and effective utilization of lithium ion battery. State of charge (SOC) is key parameter of battery management system. SOC estimation isn’t an easy job. Effective estimation of SOC involves complex algorithms where. Conventional methods of SOC estimation does not take continuously varying battery parameters into account thus large noise in both voltage and current signal are observed resulting in inaccurate estimation of SOC. Therefore, in order to improve the accuracy and precision in SOC estimation, improved adaptive algorithms with better filtering are employed and discussed in this paper. These adaptive algorithms calculate time varying battery parameters and SOC estimation are performed while bringing both time scales into account. These time scales may be slow-varying characteristics or fast-varying characteristics of battery. Some experimentations papers have proved that these adaptive filter algorithms protect battery from severe degradation and accurately calculate battery SOC. This paper reviews all previously known adaptive filter algorithms, which is the future of the electrical vehicles. At the end, a comparison is built based upon recent papers which talked on SOC at their differences in control strategies, efficiency, effectiveness, reliability, computational time and cost.
Impacts of relay and direct links at destinations in full-duplex non-orthogonal multiple access system Dinh-Thuan Do; Tu-Trinh Thi Nguyen
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp269-277

Abstract

In this study, one of effective methods of multiple access, namely non-orthogonal multiple access (NOMA), is investigated. Such NOMA scheme can be worked with signal processing at downlink side. As such, the base station sends mixed signals of two signals to destinations. A near user could be a relay to forward the signal to the distant user by leveraging benefits of full-duplex mode which allows relay to transmit and receive signals in the same time. For simple analysis, the two-user approach and fixed power allocation factors are implemented. We also derive formulas of the outage probability of two users (near-user and far-user) to indicate fairness and emphasize the role of the near user as a relay. This considered NOMA system adopts transmission with Nakagami-m fading channel. As a further metric, throughput is considered under the impacts of key system parameters. The transmit signal-to-noise ratio (SNR) at the base station make influences the performance of two users significantly as observation indicated in our simulation results. These results are confirmed by matching Monte-Carlo with the theoretical simulations.
Tracking control for planar nonminimum-phase bilinear control system with disturbance using backstepping Khozin Mu`tamar; Janson Naiborhu; Roberd Saragih; Dewi Handayani
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1315-1327

Abstract

This article presents the design control of a tracking problem for a non-minimum phase bilinear control system containing disturbance. The bilinear control system is assumed to have a relative degree one and non-minimum phase, which means it has unstable internal dynamics. The disturbance exists only in state variables corresponding to the control function in external dynamics. The control design was carried out using the backstepping method, which was applied to the normal form of the bilinear control system. Internal dynamics will be stabilised using virtual control to overcome unstable internal dynamics. The last step will stabilise the external dynamics and disturbance using the original control function. The simulation results show that the proposed control method can rapidly drive the output to the given trajectory. Control performance varies depending on the control parameter setting. The higher the control parameter, the better the control performance, evaluated using integral absolute error.
Automatic deception detection system based on hybrid feature extraction techniques Shaimaa Hameed Abd; Ivan A. Hashim; Ali Sadeq Abdulhadi Jalal
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp381-393

Abstract

Human face is considered as a rich source of non-verbal features. These features have proven their efficiency, so they are used by the deception detection system (DDS) to distinguish liar from innocent subjects. The suggested DDS utilized three kinds of features, these are facial expressions, head movements and eye gaze. Facial expressions are simply encoded and represented in the form of action units (AUs) based on facial action coding system (FACS). Head movements are represented based on both transitions and rotation. For eye gaze features, the eye gaze directional angle in both x-axis and y-axis are extracted. The collected database used to prove validity and robustness of the suggested system contains videos for 102 subjects from both genders with age range 18-55 years. The detection accuracy of the suggested DDS based on applying the logistic regression classifier is equal to 88.0631%. The proposed system has proven its robustness and the achievement of the highest detection accuracy when compared with previously designed systems.
Big data based architecture to bringing together graduates and recruiters: case of Moroccan University Abdemounaime Hamdane; Nadir Belhaj; Halima El Hamdaoui; Karima Aissaoui; Moulhime El Bekkali; Nour El Houda Chaoui
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1701-1709

Abstract

Due to the current health crisis caused by covid 19, a negative impact has occurred on the global economy and more specifically on employability. Many people have lost their jobs or have seen their incomes drop. Nowadays, the search for job offers or potential candidates is done mainly online, where several platforms already exist (LinkedIn, Viadeo or others online recruitment systems). These solutions are particularly difficult to use due to the volume of data to be found and the manual compatibility check. In addition, the surplus of unqualified candidates and unverified resumes is a major concern of online recruiting systems. What we propose in this article is a framework that helps bridge the gap between graduates and recruiters through a big data architecture for university based on a real and certified database of graduates and companies.
Analysing most efficient deep learning model to detect COVID-19 from computer tomography images F.M. Javed Mehedi Shamrat; Sovon Chakraborty; Rasel Ahammad; Tanzil Mahbub Shitab; Md.Aslam Kazi; Alamin Hossain; Imran Mahmud
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp462-471

Abstract

COVID-19 illness has a detrimental impact on the respiratory system, and the severity of the infection may be determined utilizing a selected imaging technique. Chest computer tomography (CT) imaging is a reliable diagnostic technique for finding COVID-19 early and slowing its progression. Recent research shows that deep learning algorithms, particularly convolutional neural network (CNN), may accurately diagnose COVID-19 using lung CT scan images. But in an emergency, detection accuracy simply is not enough. Determinants of data loss and classification completion time play a critical element. This study addresses the issue by finding the most efficient CNN model with the least data loss and classification time. Eight deep learning models, including Max Pooling 2D, Average Pooling 2D, VGG19, VGG16, MobileNetV2, InceptionV3, AlexNet, NFNet using a dataset of 16000 CT scans image data of COVID-19 and non-COVID-19 are compared in the study. Using the confusion matrix, the performance of the models is compared and together with the data loss and completion time. It is observed from the research that MobileNetV2 provides the highest accurate result of 99.12% with the least data loss of 0.0504% in the lowest classification completion time of 16.5secs per epoch. Thus, employing MobileNetV2 gives the best and the quickest result in an emergency.
Smart-geofencing for system of reporting inadequate regional infrastructure using crossing and winding number Puspa Miladin Nuraida Safitri A. Basid; Fresy Nugroho
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1662-1671

Abstract

Infrastructure is a major factor in increasing regional productivity and become the center of economic growth. problems arise when infrastructure improvements are only centered on downtown areas. One of the ways to develop regional infrastructure is with media reporting inadequate infrastructure. The use of smartphone technology and geofencing techniques are considered to be helpful in reporting media. Increasingly sophisticated built-in features in smartphones are undeniable proof of the technology’s highly impressive advancement. Mobile applications utilizing location-based services (LBS) assisted by a global positioning systems (GPS) sensor are rapidly becoming popular. One of its most-favorited features is geofencing. The volunteered-geographic-information (VGI)-based reporting system for regional infrastructure damage uses this feature to obtain the data input directly from the users. In this current research, the author uses two methods in geofencing, including crossing number (CN) and winding number (WN). Several previous studies suggest that the combination of these methods might result in faster computation, compared to the raycasting method. By using winding and crossing number methods, 98% of increased accuracy can be obtained, while a prior method could only obtain 96%. An improvement in the system’s speed is also expected.
Transformer based multi-head attention network for aspect-based sentiment classification Abhinandan Shirahatti; Vijay Rajpurohit; Sanjeev Sannakki
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp472-481

Abstract

Aspect-based sentiment classification is vital in helping manufacturers identify the pros and cons of their products and features. In the latest days, there has been a tremendous surge of interest in aspect-based sentiment classification (ABSC). Since it predicts an aspect term sentiment polarity in a sentence rather than the whole sentence. Most of the existing methods have used recurrent neural networks and attention mechanisms which fail to capture global dependencies of the input sequence and it leads to some information loss and some of the existing methods used sequence models for this task, but training these models is a bit tedious. Here, we propose the multi-head attention transformation (MHAT) network the MHAT utilizes a transformer encoder in order to minimize training time for ABSC tasks. First, we used a pre-trained Global vectors for word representation (GloVe) for word and aspect term embeddings. Second, part-of-speech (POS) features are fused with MHAT to extract grammatical aspects of an input sentence. Whereas most of the existing methods have neglected this. Using the SemEval 2014 dataset, the proposed model consistently outperforms the state-of-the-art methods on aspect-based sentiment classification tasks.
The adoption factors of two-factors authentication in blockchain technology for banking and financial institutions Amir Aizzat Basori; Nor Hapiza Mohd Ariffin
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1758-1764

Abstract

Malaysian banks and financial organisations urgently require a secure authentication mechanism. However, there is a lack of research on the factors that drive blockchain authentication technology adoption, notably in Malaysian banks. This study identified the factors impacting adopting blockchain authentication technology in Malaysia. This document will be a roadmap for replacing existing technology utilizing the traditional transaction authorization code (TAC) via a short messaging service (SMS). In addition, this study looks into the elements that influence the new blockchain authentication technology's acceptability in Malaysia. The data was gathered from articles and research papers written by other scholars on blockchain authentication. To examine and categorise the aspects that influence the acceptance of blockchain authentication technology, we used risk management in technology (RMiT) standards to map them. Based on the result, security risk, regulatory support, technology latency, and technology complexity have been established as components of blockchain authentication adoption factors that can be a guideline to implement blockchain authentication in banking and financial institutions in Malaysia. In addition, the findings can contribute as a reference for future researchers to develop models or guidelines for blockchain authentication methods in banking and financial institutions.

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