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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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Telemedicine for silent hypoxia: Improving the reliability and accuracy of Max30100-based system Nila Novita Sari; Mina Naidah Gani; Regina Aprilia Maharani Yusuf; Riko Firmando
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1419-1426

Abstract

This study aims to design, assess, and improve the reliability of the telemedicine-based system for examination and monitoring of the symptoms of silent hypoxia–an extraordinary symptom of COVID-19. We design a telemedicine system for heart rate and oxygen saturation measurement which consists of a photoplethysmograph Max30100 sensor, NodeMCU microcontroller, real-time clock module, firebase realtime database, and Android-based mobile application. The designed system is tested through a comparative test with a commercially available oximeter. A total of 85 experiments from 40 participants in two different positions were conducted. Our analysis shows the accuracy rate of the Max30100 measurement is 97.11% and 98.84%, for heart rate and oxygen saturation (SpO2), respectively. Bland Altman was used to appraising and visualizing the agreement between the two measurement devices. We further apply calibration to improve the accuracy of the collected data through linear regression, which reveals 97.14% and 99% accuracy data for heart rate and SpO2, respectively. Finally, a series of end-to-end remote testing is successfully conducted representing the real-life scenario of the telemedicine system. Overall, the designed system attains a reliable option for a telemedicine-based system for examination of the symptoms of silent hypoxia.
Optimization of wind solar and battery hybrid renewable system using backtrack search algorithm Ingudam Chitrasen Meitei; Rajen Pudur
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1269-1277

Abstract

Penetration of renewable sources to the grid is always a problem for electrical engineers, apart from reliability and efficiency, cost optimization is also a big concern among them. Wind, solar and battery hybrid combinations (WSB-HPS) are also very common among hybrid systems, but this WSBHPS combines wind and solar energy power generation reduces the charge and discharge time of the battery. Therefore, this system improves the reliability of the power supply by fully utilizing the wind and solar power generation and improves the charging and discharging state of the battery and hence reduces the whole cost as the investment in battery is reduced. backtrack search algorithm (BSA) is the highly efficient and powerful algorithm to solve combinatorial optimization problems. In this paper an attempt is made to optimize the hybrid combination using BSA in the matrix laboratory (MATLAB) environment and comparable study is made using HOMER. A complete optimised data is generated for a particular area in Manipur and reduced cost is suggested.
Development of an IoT-based water and power monitoring system for residential building Leah Santos; John Carlo Bautista; Matt William Estanque; Christian John Paloa; Ana Beatrice Villaran
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp744-751

Abstract

This study provides information between tenants and landlords on the use of the Internet of Things for power and water monitoring systems. It is one way to make reading meters and water meters easier to access using the available internet connection. The developed application using the android studio software is installed on a smartphone/tablet and verified to fully working on android versions from 4.1 (jellybean) to android 9.0 (pie). Tests were carried out in a household where the prototype was installed in a residential apartment. The data collected was monitored in the application and viewed by tenants and landlords. The results from the mean comparison of the power and volume readings measured by the wattmeter and water meter claim that the readings from the conventional meters and designed prototype have no significant difference using the Mann-Whitney U test. Evaluations were conducted showing that the device and the developed application using IoT is reliable, accurate, functional and user-friendly to use by tenants and landlords.
An accurate technique for supervising distance relays during power swing Loai Mohamed Ali El-Sayed; Doaa Khalil Ibrahim; Mahmoud Ibrahim Gilany; Aboul’Fotouh El’Gharably
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 3: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i3.pp1279-1290

Abstract

Power swing is a power system transient phenomenon that arises due to several reasons including line switching, line outage, sudden increment or decrement in load, faults, etc. Unnecessary tripping during power swing and unnecessary blocking for faults occur during power swing result in distance relay maloperation. Several cascaded outages and major worldwide blackouts have occurred due to maloperation of distance relays. This paper proposes a technique for supervising distance relays during power swing. The proposed online technique discriminates real faults and power swing accurately. It relies on constructing a locus diagram for the current and voltage differences (∆I-∆V) between the two ends of the protected line. The locus is estimated at every power frequency cycle to continuously monitor the state of the line by utilizing the synchrophasor measurements at the sending and receiving ends of the line. The proposed technique is tested for two-area, four-machine power system under faults at different locations of zone-1 and zone-2 regions of distance relays, fault resistances, fault inception angles and slip frequencies using MATLAB software. The simulation results proved the superior improvement of distance relay performance for handling power swing blocking and unblocking actions.
Corpus-based technique for improving Arabic OCR system Ahmed Hussain Aliwy; Basheer Al-Sadawi
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 1: January 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i1.pp233-241

Abstract

An optical character recognition (OCR) refers to a process of converting the text document images into editable and searchable text. OCR process poses several challenges in particular in the Arabic language due to it has caused a high percentage of errors. In this paper, a method, to improve the outputs of the Arabic Optical character recognition (AOCR) Systems is suggested based on a statistical language model built from the available huge corpora. This method includes detecting and correcting non-word and real words error according to the context of the word in the sentence. The results show that the percentage of improvement in the results is up to (98%) as a new accuracy for AOCR output. 
Predicting students' learning styles using regression techniques Ahmad Mousa Altamimi; Mohammad Azzeh; Mahmoud Albashayreh
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp1177-1185

Abstract

Traditional learning systems have responded quickly to the COVID pandemic and moved to online or distance learning. Online learning requires a personalization method because the interaction between learners and instructors is minimal, and learners have a specific learning method that works best for them. One of the personalization methods is detecting the learners' learning style. To detect learning styles, several works have been proposed using classification techniques. However, the current detection models become ineffective when learners have no dominant style or a mix of learning styles. Thus, the objective of this study is twofold. Firstly, constructing a prediction model based on regression analysis provides a probabilistic approach for inferring the preferred learning style. Secondly, comparing regression models and classification models for detecting learning style. To ground our conceptual model, a set of machine learning algorithms have been implemented based on a dataset collected from a sample of 72 students using visual, auditory, reading/writing, and kinesthetic (VARK's) inventory questionnaire. Results show that regression techniques are more accurate and representative for real-world scenarios than classification algorithms, where students might have multiple learning styles but with different probabilities. We believe that this research will help educational institutes to engage learning styles in the teaching process.
AlGaInP optical source integrated with fiber links and silicon avalanche photo detectors in fiber optic systems Mahmoud M. A. Eid; Shabana Urooj; Norah Muhammad Alwadai; Ahmed Nabih Zaki Rashed
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp847-854

Abstract

This study has clarified aluminium gallium indium phosphide (AlGaInP) optical source integrated with fiber links and silicon avalanche photodetectors in fiber optic systems. The output spectral power, rise time, signal frequency and resonance frequency for AlGaInP laser diode. The laser diode rise time, output spectral power and resonance/signal frequencies versus injection current and ambient temperatures are sketched. The silica doped germanium fiber link pulse broadening and the signal fiber bandwidth are investigated against temperature variations. The signal per noise ratio is related to Q value and bit error rate (BER) at the receiving point (Si avalanche photodetector (APD)) are sketched with temperature.
Sentiment analysis of Twitter posts related to the COVID-19 vaccines Noralhuda N. Alabid; Zainab Dalaf Katheeth
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1727-1734

Abstract

A real threat to the people of the world has appeared as a result of the spread of the Coronavirus disease of 2019 (COVID-19) disease. A lot of scientific and financial support has been made to devote vaccines capable of ending this epidemic. However, these vaccines have become a subject of debate between individuals, as some people tend to support taking vaccines and others rejecting them. This paper aims to create a framework model to classify the sentiment and opinions of individuals that published in Twitter regarding the COVID-19 vaccines. Identify those opinions can help public health institutions to know public opinions and direct their efforts towards promoting taking vaccinations. Two of the machines learning classification models which are the support vector machine (SVM) and naive Bayes (NB) classifier are applied here. Other pre-processing methods were applied as well to filter unstructured tweets.
A survey of distance learning in Morocco during COVID19 Sara Ouahabi; Kamal El Guemmat; Mohamed Azouazi; Sanaa El Filali
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp1087-1095

Abstract

The face-to-face mode is always considered as the normal mode of teaching, and distance education is often understood as a remedy for the lack of material and human resources necessary to conduct training; but to prevent the spread of the coronavirus (COVID19), the distance course system has been launched in different countries to ensure continuity of teaching during the period when courses are stopped. In order to shed light on the role of distance learning during the spread of the coronavirus and its effectiveness in successfully continuing the learning process, an investigation was carried out in the Moroccan context. This survey was launched as a questionnaire with 565 participants; they are students and teachers from primary, secondary, university and professional training. The objective is to answer several research questions concerning the current use of distance education during the COVID19 pandemic. The results of this survey are presented in this article as well as their analysis showing that solutions and alternatives must be adopted in order to improve the teaching and learning process in the event of a situation like COVID19.
Classification of rice plant nitrogen nutrient status using k-nearest neighbors (k-NN) with light intensity data Muliady Muliady; Lim Tien Sze; Koo Voon Chet; Suhadra Patra
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp179-186

Abstract

Crop management including the efficient use of nitrogen (N) fertilizer is important to ensure crop productivity. Human error in judging the leaf greenness when using the leaf color chart (LCC) to estimate the rice plant N nutrient status has encouraged numerous researchers to implement a machine-learning algorithm but experienced some issues in calibration and lighting. The datasets are created at 6.00-7.00AM (consistent lighting) and including light intensity, so each dataset contains RGB value and light intensity as inputs, and LCC value as a target. A system consists of a smartphone with an application that prevents user from taking an image if the light intensity is not in 2000-3500 lux, and a computer for preprocessing and classification purposes were developed. The preprocessing included cropping, splitting the rice leaf images, and calculating the average RGB values. A k-NN classifier is implemented and by using a cross-validation method is found k=5 gives the best accuracy of 97,22%. The in-site test of the system also works with an accuracy of 96.40%. 

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