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INDONESIA
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Published by IAES Indonesia Section and Partners
ISSN : 20893272     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is a peer reviewed International Journal in English published four issues per year (March, June, September and December). The aim of Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the engineering of Telecommunication and Information Technology, Applied Computing & Computer, Instrumentation & Control, Electrical (Power), Electronics, and Informatics.
Arjuna Subject : -
Articles 19 Documents
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Search results for , issue "Vol 10, No 1: March 2022" : 19 Documents clear
An Ensemble Classifier Based on Individual Features for Detecting Microaneurysms in Diabetic Retinopathy Mohamed Jebran Pendekal; Shweta Gupta
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 1: March 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i1.3522

Abstract

Individuals with diabetes are more likely to develop Diabetic Retinopathy (DR), a chronic ailment that can lead to blindness if left undiagnosed. Early-stage Diabetic Retinopathy (DR) is characterized by Microaneurysms (MA), which appear as tiny red lesions on the retina. This paper investigates a unique approach for the automated early identification of microaneurysms  in eye images. A unique ensemble classifier technique is suggested in this work. Classifiers like SVM, KNN, Decision Tree, and Naïve Bayes are chosen in this study for building an ensemble model. After preprocessing the image, certain common image characteristics such as shape and intensity features were retrieved from the candidate. The mean absolute difference of each feature is computed. Based on mean ranges that would give improved classification results, an expert classifier is chosen and trained. The outputs of the classifiers are integrated for each of the distinct characteristics, and the number of categories that have been most frequently repeated is utilized to reach a final decision. The process has been comprehensively validated using two available open datasets, like e-ophtha and DIARETDB1. On the e-ophtha and DIARETDB1 datasets, the ensemble model achieved an AUC of 0.928 and 0.873, Sensitivity of 90.7% and 85%, Specificity of 90% and 91% respectively.
Optimal RoadSide Units Distribution Approach in Vehicular Ad hoc Network Ali Kies; Khedidja Belbachir; Zoulikha Mekkakia Maaza; Claude Duvallet
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 1: March 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i1.3116

Abstract

A vehicular ad hoc network is a particular type of ad hoc mobile network. It is characterized by high mobility and frequent disconnection between vehicles. For this, the roadside units (RSUs) deployment permits to enhance the network connectivity. The objective of this work is to provide an optimized RSUs placement for enhancing the network connectivity and maximizing the accident coverage with reducing the deployment cost. In this paper, we propose our approach called Optimized RoadSide units Deployment (ORSD). The proposed approach comprises a two-step, in the first step, ORSD finds the RSUs candidate locations based on network density and connectivity. We calculated the connectivity of each segment based on speed and arrival information’s.  The second step permit to find the optimal solution of our proposed objective function. The objective function permits to enhance the network connectivity and maximizing the accident coverage.  To find the optimal solution of our objective function is an NP-complete problem of order o(n²) .  Therefore, we propose to solve this problem in two phases, so that it becomes a simple linear problem to solve. The ORSD is proposed for urban and high way scenarios. The extensive simulation study is conducted in order to assess the effectiveness of the proposed approach. We use the Simulator of Urban MObility (SUMO) for generating different traffic scenarios. We develop scripts to extract different information as density, speed and travel time in each segment. Then, we develop an algorithm to calculate connectivity probability for each segment. Then, we implement our objective function to finds optimal RSUs positions in terms of connectivity, accident cover and cost.
Study of the Alamouti-OFDM system using ZP technique and training symbols in multi path selective fading channel Hadj Ali Bakir; Fatima Debbat; Fethi Tarik Bendimerad
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 1: March 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i1.1745

Abstract

In this paper, we propose a modified Alamouti code matrix and it associated with zero padding orthogonal frequency division multiplexing known as (Alamouti- ZP OFDM). Which zero padding (or zeros samples) are adopted over the OFDM symbols that construct also the encoded symbols of the Alamouti matx. Training symbols are applied for the channel estimation. Furthermore, the ML decoding algorithm  is used to get output bits which the BER can be measured. Using the selective multi path fading as model for wireless channel to evaluate the performance provided by the system proposed. The performance of  the approach proposed is based on BER parameter. For that, the system  is simulated  in two profiles of paths number (3 paths, and 6 paths) have used, which the spread delays of these paths are taken (in millisecond and in microsecond) respectively. Different data stream are simulated and compared. And the BER performance are compared also for ifft lengths 512 and 1024 and the BER results presented for all parameters of (paths number, and spread delays). The simulation  results show that the system  presented performed good even the spread delays of multi path channel are great (microseconds or milliseconds) and even increased  the data simulated from  increasing the parallel of the data streams transmitted in the system study . So, the system could keen their effectiveness against of fading channel and ISI phenomenon.  And finaly, it is shown that increasing IFFT samples in the simulation process the improvements are more enhanced of the approach proposed.
A Novel Framework To Investigate The Impact Of Social Media Advertising Features On Customer Purchase Intention Using Bwo-Dann Meena Zenith N
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 1: March 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/.v10i1.2993

Abstract

Social Media (SM) has turned out to be a platform for marketing as well as advertising activities. In relation to SM Advertising (SMA), the cultural influence on consumers’ behavior as well as attitude is more vital. Organizations have used up loads of money, time, and also resources on SMA. Nevertheless, it is always a challenge for the organizations to model SM advertisement in a means to effectively attract and also motivate customers into purchasing their brands. This paper proposed a novel framework to scrutinize the SMA features’ impact on Customer Purchase Intention (CPI) by means of the BWO-DANN. Initially, the questionnaires are given to the various customer and their answers are collected. Then, answers will be uploaded and are converted into numerical format into the system. Next, the CSGA-KM is utilizedfor clustering the questionnaires on the base of personal information. Then the BWO-DANN is utilized to train the converted questionnaire set. After that, the system is tested by utilizing KFCV. Finally, through the mean model, CPI is founded out. The extensive experimentation’s outcomes illustrated that the system trounced the other methodologies, and also it is best to examine the CPI.
Reliability Evaluation of 33/11kV Olunde Injection Substation for Improved Performance Olayinka Matthew Oyeleye; Olatunde Adeyemo; Thomas Olabode Ale
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 1: March 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i1.3688

Abstract

Electric power supply reliability (PSR) is crucial in the present economy to avoid huge economic loss and life discomfort. Thus, there is need to improve PSR. Reliability evaluation of power equipment was carried out on Olunde 33/11kV Injection Substation using extracted and available five years data of Fault Frequency and Downtime of associated power equipment for 2016 to 2020 from the Injection Substation’s log books. Fault Tree Analysis (FTA) Technique was used in this research. The existing Injection Power Substation results shows that the overall injection substation unavailability of power supply was 0.00672; 33kV NBL Feeder alone has the highest percentage of failure contribution 72.97% of the unavailability of the injection substation. The reliability improvement of the Injection Power Substation using doubling maintenance activities method on the NBL 33kV feeder shows that the overall injection substation unavailability improved from 1:1.57; the NBL 33kV feeder failure contribution reduced to 15.63%. Using a redundant feeder, 33kV feeder, the overall unavailability of the injection substation improved from 1:3.6; the NBL 33kV failure contribution reduced to 1.31%. The redundant feeder approach in this work is highly significant since it is better than the doubling maintenance activities method.
An efficient human activity recognition model based on deep learning approaches Aymen Jalil Abdulelah; Mohannad Al-Kubaisi; Ahmed Muhi Shentaf
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 1: March 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i1.3438

Abstract

Human Activity Recognition (HAR) has gained traction in recent years in diverse areas such as observation, entertainment, teaching and healthcare, using wearable and smartphone sensors. Such environments and systems necessitate and subsume activity recognition, aimed at recognizing the actions, characteristics, and goals of one or more individuals from a temporal series of observations streamed from one or more sensors. Different developed models for HAR have been explained in literature. Deep learning systems and algorithms were shown to perform highly in HAR in recent years, but these algorithms need lots of computerization to be deployed efficiently in applications. This paper presents a HAR lightweight, low computing capacity, deep learning model, which is ideal for use in real-time applications. The generic HAR framework for smartphone sensor data is proposed, based on Long Short-Term Memory (LSTM) networks for time-series domains and standard Convolutional Neural Network (CNN) used for classification. The findings demonstrate that many of the deployed deep learning and machine learning techniques are surpassed by the proposed model. TRANSLATE with x English ArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back //
Stock Prediction Based on Twitter Sentiment Extraction Using BiLSTM-Attention Dhomas Hatta Fudholi; Royan Abida N. Nayoan; Septia Rani
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 1: March 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i1.3011

Abstract

A profitable stock price prediction will yield a large profit. According to behavioural economics, other people's emotions and viewpoints have a significant impact on business. One of them is the rise and fall of stock prices. Previous studies have shown that public sentiments retrieved from online information can be very valuable on market trading. In this paper, we propose a model that works well in predicting future stock prices by using public sentiments from social media. The online information used in this research is financial tweets collected from Twitter and the stock prices values retrieved from Yahoo! Finance. We collected tweets related to Netflix Company stocks and the stock prices for the same period which is 5 years from 2015 to 2020 as the dataset. We extracted the sentiment value using VADER algorithm. In this paper, we apply a Bidirectional Long Short-Term Memory (BiLSTM) architecture to achieve our goal. Moreover, we created seven different experiments with different stock price parameters and selected sentiment values combinations and investigated the model by adding an attention layer. We experimented with two different sentiment values, tweet’s compound value and tweet’s compound value multiplied by favorites count. We considered the favorites count as one representation of public sentiments. From the seven experiments, the experiment with Bidirectional Long Short-Term Memory (BiLSTM) - attention model combined with our selected stock price parameters namely close price, open price, and using Twitter sentiment values that are multiplied with the tweet’s favorites count yields a better RMSE result of 2.482e-02 in train set and 2.981e-02 in the test set.
A Collision Avoidance Based Energy Efficient Medium Access Control Protocol for Clustered Underwater Wireless Sensor Networks K. R. Narasimha Murthy; Govind R Kadambi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 1: March 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i1.3633

Abstract

Underwater Wireless Sensor Networks (UWSNs) are typically deployed in energy constrained environments where recharging energy sources and replacing batteries are not viable. This makes energy efficiency in UWSNs a crucial directive to be followed during Medium Access Control (MAC) design. Multiplexing and scheduling based protocols are not ideal for UWSNs because of their strict synchronization requirements, longer latencies and constrained bandwidth.This paper presents the development and simulation analysis of a novel cross-layer communication based MAC protocol called Energy Efficient Collision Avoidance (EECA) MAC protocol. EECA-MAC protocol works on the principle of adaptive power control, controlling the transmission power based on the signal strength at the receiver. EECA-MAC enhances the conventional 4-way handshake to reduce carrier sensing by implementing an enhanced Request to Send (RTS) and Clear to Send (CTS) handshake and an improved back-off algorithm.Simulation analysis shows that the measures taken to achieve energy efficiency have a direct effect on the number of packet retransmissions. Compared to the Medium Access with Collision Avoidance (MACA) protocol, EECA-MAC shows a 40% reduction in the number of packets that are delivered after retransmissions. This reduction, coupled with the reduced signal interference, results in a 16% drop in the energy utilized by the nodes for data transmission.
Sentiment Analysis in Karonese Tweet using Machine Learning Ichwanul Muslim Karo Karo; Mohd Farhan Md Fudzee; Shahreen Kasim; Azizul Azhar Ramli
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 1: March 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i1.3565

Abstract

Recently, many social media users expressed their conditions, ideas, emotions using local languages ​​on social media, for example via tweets or status. Due to the large number of texts, sentiment analysis is used to identify opinions, ideas, or thoughts from social media. Sentiment analysis research has also been widely applied to local languages. Karonese is one of the largest local languages ​​in North Sumatera, Indonesia. Karo society actively use the language in expression on twitter. This study proposes two things: Karonese tweet dataset for classification and analysis of sentiment on Karonese. Several machine learning algorithms are implemented in this research, that is Logistic regression, Naive bayes, K-nearest neighbor, and Support Vector Machine (SVM). Karonese tweets is obtained from timeline twitter based on several keywords and hashtags. Transcribers from ethnic figures helped annotating the Karo tweets into three classes: positive, negative, and neutral. To get the best model, several scenarios were run based on various compositions of training data and test data. The SVM algorithm has highest accuracy, precision, recall, and F-1 scores than others. As the research is a preliminary research of sentiment analysis on Karonese language, there are many feature works to improvement.

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