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Proceeding of the Electrical Engineering Computer Science and Informatics
Published by Institute of Advanced Engineering and Science
ISSN : 2407439X     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Proceeding of the Electrical Engineering Computer Science and Informatics publishes papers of the "International Conference on Electrical Engineering Computer Science and Informatics (EECSI)" Series in high technical standard. The Proceeding is aimed to bring researchers, academicians, scientists, students, engineers and practitioners together to participate and present their latest research finding, developments and applications related to the various aspects of electrical, electronics, power electronics, instrumentation, control, computer & telecommunication engineering, signal processing, soft computing, computer science and informatics.
Arjuna Subject : -
Articles 649 Documents
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Semantic Classification of Scientific Sentence Pair Using Recurrent Neural Network Agung Besti; Ridwan Ilyas; Fatan Kasyidi; Esmeralda Contessa Djamal
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2051

Abstract

One development of Natural Language Processing is the semantic classification of sentences and documents. The challenge is finding relationships between words and between documents through a computational model. The development of machine learning makes it possible to try out various possibilities that provide classification capabilities. This paper proposes the semantic classification of sentence pairs using Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM). Each couple of sentences is turned into vectors using Word2Vec. Experiments carried out using CBOW and Skip-Gram to get the best combination. The results are obtained that word embedding using CBOW produces better than Skip-Gram, although it is still around 5%. However, CBOW slows slightly at the beginning of iteration but is stable towards convergence. Classification of all six classes, namely Equivalent, Similar, Specific, No Alignment, Related, and Opposite. As a result of the unbalanced data set, the retraining was conducted by eliminating a few classes member from the data set, thus providing an accuracy of 73% for non-training data. The results showed that the Adam model gave a faster convergence at the start of training compared to the SGD model, and AdaDelta, which was built, gave 75% better accuracy with an F1-Score of 67%.
Classification of Post-Stroke EEG Signal Using Genetic Algorithm and Recurrent Neural Networks Ella Wahyu Guntari; Esmeralda Contessa Djamal; Fikri Nugraha; Sandi Lesmana Liem
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2052

Abstract

Stroke is caused by a sudden burst of blood vessels in the brain, causing speech difficulties, memory loss, and also paralysis. The identification of electrical activity in the brain of post-stroke patients from EEG signals is an attempt to evaluate rehabilitation. EEG signal recording involves multiple channels with overlapping information. Therefore the importance of channel optimization is to reduce processing time and reduce the computational burden. Besides, that channel optimization can have an overfitting effect due to excessive utilization of EEG channels. This paper proposed the optimization of EEG channels for the identification of post-stroke patients using Genetic Algorithms and Recurrent Neural Networks. Data was taken from 75 subjects with a recording duration of 180 seconds in a seated state. The data was segmented and extracted using Wavelet to get the frequency of the Alpha, Theta, Mu, Delta, and Amplitude changes. The next step is the channel optimization process using Genetic Algorithms. The method applied to get a combination of channels that qualifies. Then, the EEG signal identification proceeds of the optimization of the channels used Recurrent Neural Network. The result showed that applying the Genetic Algorithm afforded 12 channels configuration with 90.00% of accuracy; meanwhile, used all channels gave a 72.22% result. Therefore, channel optimization is essential to reduce redundancy and increase recognition.
IoT in Patient Respiratory Condition & Oxygen Regulator's Flowrate Monitor Ayu Jati Puspitasari; Arya Nicosa; Dian Bayu Prakarsa; Djiwo Harsono
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2053

Abstract

Respiratory condition monitoring, including respiration rate and oxygen saturation, and oxygen flowrate in oxygen tanks needed for patients undergoing oxygen therapy. Lack of medical staff in hospitals and efforts to minimize interactions between patients and nurses during the pandemic, open opportunity to develop the respiratory condition and oxygen flowrate monitoring systems using the Internet of Things (IoT) technology. Respiration rate and oxygen saturation data send to the local web network using the ESP8266 WiFi module and router. This monitoring system website was built on a server computer in the monitoring room using the PHP-MySQL programming language with Sublime Text 3 and XAMPP software. The website consists of features of the new user registration, user login, adding patient data, editing patient data, searching for patients, and patient respiratory condition monitoring pages. Connection speed based on the ability of the router range and the distance between the router and the microcontroller. For testing the reliability of the connection, the system simulated interrupted. The reconnecting times for the router and microcontroller range 3, 5, and 7 meters are 35.4 s, 35.6 s, and 35.3 s, respectively. The average response time for the system to receive data from the microcontroller and display the data on the monitoring page is 1.998 s, and there is no different data from the data on the web database and data on the serial monitor.
A Wireless ECG Device with Mobile Applications for Android Mohamad Hafis Nornaim; Nurul Ashikin Abdul-Kadir; Fauzan Khairi Che Harun; Mohd Azhar Abdul Razak
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2054

Abstract

Electrocardiograph (ECG) is a measuring device that used in hospital to monitor electrical activity of heart. Commonly used ECG device is a Holter monitor, a portable and wired device, which is bulky and not suitable for measuring and recording athlete's heart activity during training. The objective of this study was to design the ECG monitoring system as an Internet of Things (IoT) device, equipped with a temperature detector to detect user's body temperature. The ECG signals and the temperature were transmitted wirelessly using Bluetooth transmission to the mobile applications (apps). Both signals were set to display on mobile apps which was developed using Blynk application. At the end of this project, the signals were shown on the mobile apps and the user could monitor their own ECG signals as well as to share with their caretaker or physician later.
Hand Movement Identification Using Single-Stream Spatial Convolutional Neural Networks Aldi Sidik Permana; Esmeralda Contessa Djamal; Fikri Nugraha; Fatan Kasyidi
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2055

Abstract

Human-robot interaction can be through several ways, such as through device control, sounds, brain, and body, or hand gesture. There are two main issues: the ability to adapt to extreme settings and the number of frames processed concerning memory capabilities. Although it is necessary to be careful with the selection of the number of frames so as not to burden the memory, this paper proposed identifying hand gesture of video using Spatial Convolutional Neural Networks (CNN). The sequential image's spatial arrangement is extracted from the frames contained in the video so that each frame can be identified as part of one of the hand movements. The research used VGG16, as CNN architecture is concerned with the depth of learning where there are 13 layers of convolution and three layers of identification. Hand gestures can only be identified into four movements, namely 'right', 'left', 'grab', and 'phone'. Hand gesture identification on the video using Spatial CNN with an initial accuracy of 87.97%, then the second training increased to 98.05%. Accuracy was obtained after training using 5600 training data and 1120 test data, and the improvement occurred after manual noise reduction was performed.
The Improvement Impact Performance of Face Detection Using YOLO Algorithm Rakha Asyrofi; Yoni Azhar Winata
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2056

Abstract

Image data augmentation is a way that makes it possible to increase the diversity of available data without actually collecting new data. In this study, researchers have evaluated the application of image manipulation with the Thatcher effect, double illusion, and inversion on the performance of face detection for data augmentation needs where the data obtained has a weakness that is the limited amount of data to create a training model. The purpose of this research is to increase the diversity of the data so that it can make predictions correctly if given other similar datasets. To perform face detection on images, it is done using YOLOv3 then comparing the accuracy results from the dataset after and before adding data augmentation.
Design of Integrated Bioimpedance Analysis and Body Mass Index for Users with Special Needs Ganjar Winasis; Munawar Riyadi; Teguh Prakoso
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2057

Abstract

This research was conducted with the aim to build integration between Bioimpedance Analysis (BIA) and Body Mass Index (BMI) for users with special needs. The proposed system can measure height, weight, BMI and body composition simultaneously to be used by the elderly population and handicapped users. The proposed system is developed as a chair equipped with several system blocks, namely BIA block, BMI block, power supply block, and microcontroller block. Before starting the measurement, users only need to enter their age and gender data. The whole system is controlled by using Arduino Mega 2560 on the microcontroller block equipped with keypad for data input and an LCD to display measurement results. System testing is performed by comparing the measurement results with Omron HBF-375. The test involved 8 volunteers (4 males and 4 females). The test results show that the integrated BIA-BMI works well with an average error of 1.5%.
Investigation of Structural Parameter Variation on Extended Gate TFET for Bio-Sensor Applications Sudipta Mukherjee; Somnath Chakraborty; Deven Diwakar; Apurba Laha; Udayan Ganguly; Swaroop Ganguly
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2058

Abstract

Traditional Gate engineered Metal Oxide Semiconductor (MOS) technology faced serious challenges in terms of greater sensitivity for target biomolecules and to be utilized as the state-of-the-art Nano-recognition tool. Research on a tunnel field-effect transistor (TFET) started with the aim to achieve fast detection, low power consumption, and its potential for on-chip integration capability. Dielectric Modulated TFET (DMTFET) has established itself to be a primary candidate for sensing both charged and charge-neutral species with volumetric sensitivity. As extended gate DMTFET happens to be inferior to its short gate counterpart, we have devised ways to achieve superior performance only by making variations over structural electrostatics. With the incorporation of most possible ways of modulation, we present two orders of magnitude on-current increment and a considerable percentage of sensitivity improvement over the conventional one. Future scopes having noteworthy diversifications have also been analyzed with proper justification.
Spoken Word and Speaker Recognition Using MFCC and Multiple Recurrent Neural Networks Yoga Utomo; Esmeralda Contessa Djamal; Fikri Nugraha; Faiza Renaldi
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2059

Abstract

Identification of spoken word and speaker has been featured in many kinds of research. The problem or obstacle that persists is in the pronunciation of a particular word. So it is the noise that causes the difficulty of words to be identified. Furthermore, every human has different pronunciation habits and is influenced by several variables, such as amplitude, frequency, tempo, and rhythmic. This study proposed the identification of spoken sounds by using specific word input to determine the patterns of the speaker and spoken using Mel-frequency Cepstrum Coefficients (MFCC) and Multiple Recurrent Neural Networks (RNN). The Mel coefficient of MFCC is used as an input feature for identifying spoken words and speakers using RNN and Long Short Term Memory (LSTM). Multiple RNN works spoken word and speaker in parallel. The results obtained by multiple RNN have an accuracy of 87.74%, while single RNNs have 80.58% using Adam of new data. In order to test our model computational regularly, the experiment tested K-fold Cross-Validation of datasets for spoken and speakers with an average accuracy of 86.07%, which means the model to be able to learn on the dataset without being affected by the order or selection of test data.
IoT-Enabled Community Care for Ageing-in-Place: The Singapore Experience Hwee Pink Tan
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2060

Abstract

The paradigm of ageing-in-place - where the elderly live and age in their own homes, independently and safely, with care provided by the community - is compelling, especially in societies that face both shortages in institutionalized eldercare resources, and rapidly ageing populations. When the number of elderly who live alone rises rapidly, support and care from their communities become increasing crucial. Internet of Things (IoT) technologies. They can become the fundamental enabler for smart community eldercare. In this presentation, I would like to share our experiences and learnings gathered from large-scale deployments of IoT systems in in-home and community spaces that elderly living alone interact with, focusing on the key insights as well as operational and usability aspects of such systems.

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