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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 :
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Articles 64 Documents
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Search results for , issue "Vol 26, No 1: April 2022" : 64 Documents clear
Low-cost nitrogen dioxide monitoring station based on metal oxide sensor and cellular network Rady Purbakawaca; Arief Sabdo Yuwono; Husin Alatas; I Dewa Made Subrata
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.pp105-115

Abstract

Air pollution has a negative impact on the environment and human health. Meanwhile, the number of conventional air quality monitoring stations is minimal due to high procurement and operational costs. This study proposes a nitrogen dioxide (NO2) pollutant measurement system using the metal oxide sensor (MOX) sensor and cellular network for data transmission in the measurement area. A calibration curve is used to measure NO2 levels based on the sensor's internal resistance changes. Measurement data of NO2 concentration, air temperature, relative humidity, and geospatial information are compiled and sent via global positioning system (GSM), general packet radio service (GPRS) radio communication with transmission intervals of every minute. The database server processes the data and displays it on the web application. System testing results at the Tugu Kujang Bogor at 15:38:00-16:38:00 September 23, 2021, showed that the concentration of NO2 ranged from 0.16-0.52ppm with an average of 270 ppb with an AQI of 133 in the unhealthy category for the sensitive group. The measured NO2 levels are outside the range of the NO2 concentration database in the industrial areas of Bogor and Jakarta for the 2016-2020 period. Therefore, this system provides an excellent opportunity to obtain real-time measurement data in the field.
Low power residue number system using lookup table decomposition and finite state machine based post computation Balaji Morasa; Padmaja Nimmagadda
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.pp127-134

Abstract

In this paper, memory optimization and architectural level modifications are introduced for realizing the low power residue number system (RNS) with improved flexibility for electroencephalograph (EEG) signal classification. The proposed RNS framework is intended to maximize the reconfigurability of RNS for high-performance finite impulse response (FIR) filter design. By replacing the existing power-hungry RAM-based reverse conversion model with a highly decomposed lookup table (LUT) model which can produce the results without using any post accumulation process. The reverse conversion block is modified with an appropriate functional unit to accommodate FIR convolution results. The proposed approach is established to develop and execute pre-calculated inverters for various module sets. Therefore, the proposed LUT-decomposition with RNS multiplication-based post-accumulation technology provides a high-performance FIR filter architecture that allows different frequency response configuration elements. Experimental results shows the superior performance of decomposing LUT-based direct reverse conversion over other existing reverse conversion techniques adopted for energy-efficient RNS FIR implementations. When compared with the conventional RNS FIR design with the proposed FSM based decomposed RNS FIR, the logic elements (LEs) were reduced by 4.57%, the frequency component is increased by 31.79%, number of LUTs is reduced by 42.85%, and the power dissipation was reduced by 13.83%.
Spam detection by using machine learning based binary classifier Mohd Fadzil Abdul Kadir; Ahmad Faisal Amri Abidin; Mohamad Afendee Mohamed; Nazirah Abdul Hamid
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.pp310-317

Abstract

Because of its ease of use and speed compared to other communication applications, email is the most commonly used communication application worldwide. However, a major drawback is its inability to detect whether mail content is either spam or ham. There is currently an increasing number of cases of stealing personal information or phishing activities via email. This project will discuss how machine learning can help in spam detection. Machine learning is an artificial intelligence application that provides the ability to automatically learn and improve data without being explicitly programmed. A binary classifier will be used to classify the text into two different categories: spam and ham. This research shows the machine learning algorithm in the Azure-based platform predicts the score more accurately compared to the machine learning algorithm in visual studio, hybrid analysis and JoeSandbox cloud.
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.
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.
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.
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.
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.
Simulating the Covid-19 epidemic event and its prevention measures using python programming Mustofa Abi Hamid; Dimas Aditama; Endi Permata; Nur Kholifah; Muhammad Nurtanto; Nuur Wachid Abdul Majid
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.pp278-288

Abstract

A simulation is needed to observe and indicate how much preventive measures influence the pandemic flow, controlling and stopping it. This study succeeded in making a stochastic susceptible infected recovered deceased (SIRD) simulation using Python programming language to determine the effectiveness of prevention methods such as masks policy, social distancing, vaccination, quarantine, and lockdown. Every preventive measure is modeled based on an equivalent actual event and every essential aspect that affects the course of the pandemic. A person is represented as a circle moving freely in two-dimensional space, and disease spreads through person-to-person contact. This simulator then tested using parameters to simulate COVID-19 and found significant results between communities that implement preventive measures and those that do not. We found that within 106 days, 284 people were infected, but when five preventive methods are applied for a total of 33 days, only 31 people were infected. Adequate to simulate epidemic events and their prevention measures, this simulator can also be used as a learning tool with factors in epidemic events such as population density, mobility, infection rate, disease mortality, and every effect of each preventive measure. Users can change and influence the simulation course using interactive and straightforward software tools.
Electricity generation from renewable energy based on abandoned wind fan Arni Munira Markom; Muhammad Hakimi Aiman Hadri; Tuah Zayan Muhamad Yazid; Zakiah Mohd Yusof; Marni Azira Markom; Ahmad Razif Muhammad
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.pp1-8

Abstract

In the 21st century, our world is facing difficult conditions for serious environmental pollution and the problem of energy shortage. An innovative idea has emerged to recycle wind energy from air conditioning condenser fans in outdoor buildings. Therefore, the main goal of this research is to develop renewable wind energy from the condenser fan of an air conditioner using Arduino as a microcontroller. This research moves towards a portable, low cost, environmentally friendly mini device that harnesses renewable energies with endless resources for future alternative power generation and reduces the burden of consumers' electricity bills.

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