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INDONESIA
Proceeding of the Electrical Engineering Computer Science and Informatics
Published by IAES Indonesia Section and Partners
ISSN : -     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 53 Documents
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Search results for , issue "Vol 7, No 1: EECSI 2020" : 53 Documents clear
Characterization of Polydimethylsiloxane Dielectric Films for Capacitive ECG Bioelectrodes Umar, Alhassan Haruna; Harun, Fauzan Khairi Che; Yusof, Yusmeeraz
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.v7.2047

Abstract

Capacitive ECG bioelectrodes are potentials for wearable and long-term physiological monitoring applications. In non-contact ECG recordings, the dielectric material sets limit to smooth bioelectric signal acquisition. Previously used dielectrics are rigid, unconformable on the skin, induce artefact and triboelectric noise, and becomes unstable when they absorb skin exudates. Recently, polymeric materials such as PDMS have gained different biomedical applications because it is biocompatible, flexible, and easy to fabricate. However, its use as a dielectric for capacitive ECG sensing is poorly reported. In this study, 15 samples of thin PDMS films of various thicknesses were fabricated by varying the proportion of the Sylgard 184TM silicone elastomer to the crosslinker from Dow Corning Corporation and manually deposited on acrylic glass substrates. The composition ratio and thickness were used to tune the structure and dielectric properties of the films. The effects on the capacitance generated by each dielectric film were measured using the parallel plate method, and their corresponding values of relative permittivity was also estimated. The results obtained reveal that PDMS films made from a composition ratio of 10:2 yielded the maximum capacitance and relative permittivity. In contrast, the film with 0.14mm thickness revealed the highest value of capacitance (31pF). The recorded values of capacitance demonstrate the feasibility of PDMS dielectrics for capacitive ECG bioelectrodes.
Dynamic Hand Gesture Recognition Using Temporal-Stream Convolutional Neural Networks Armandika, Fladio; Djamal, Esmeralda Contessa; Nugraha, Fikri; Kasyidi, Fatan
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.v7.2048

Abstract

Movement recognition is a hot issue in machine learning. The gesture recognition is related to video processing, which gives problems in various aspects. Some of them are separating the image against the background firmly. This problem has consequences when there are incredibly different settings from the training data. The next challenge is the number of images processed at a time that forms motion. Previous studies have conducted experiments on the Deep Convolutional Neural Network architecture to detect actions on sequential model balancing each other on frames and motion between frames. The challenge of identifying objects in a temporal video image is the number of parameters needed to do a simple video classification so that the estimated motion of the object in each picture frame is needed. This paper proposed the classification of hand movement patterns with the Single Stream Temporal Convolutional Neural Networks approach. This model was robust against extreme non-training data, giving an accuracy of up to 81,7%. The model used a 50 layers ResNet architecture with recorded video training.
Person tracking with non-overlapping multiple cameras Sonbhadra, Sanjay Kumar; Agarwal, Sonali; Syafrullah, Muhammad; Adiyarta, Krisna
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.v7.2049

Abstract

Monitoring and tracking of any target in a surveillance system is an important task. When these targets are human then this problem comes under person identification and tracking. At present, large scale smart video surveillance system is an essential component for any commercial or public campus. Since field of view (FOV) of a camera is limited; for large area monitoring, multiple cameras are needed at different locations. This paper proposes a novel model for tracking a person under multiple non-overlapping cameras. It builds the reference signature of the person at the beginning of the tracking system to match with the upcoming signatures captured by other cameras within the specified area of observation with the help of trained support vector machine (SVM) between two cameras. For experiments, wide area re-identification dataset (WARD) and a real-time scenario have been used with color, shape and texture features for person's re-identification.
Steady-state response feature extraction optimization to enhance electronic nose performance Agustika, Dyah Kurniawati; Hidayat, Shidiq; Triyana, Kuwat; Iliescu, Doina D; Leeson, Mark S
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.v7.2050

Abstract

Feature extraction of electronic nose (e-nose) output response aims to reduce information redundancy so that the e-nose performance can be improved. The use of different sensor types and sample targets can affect the optimization of feature extraction. This research used six types of metal oxide sensors, TGS 813, 822, 825, 826, 2620, and 2611 in an e-nose system to detect three types of herbal drink. Five kinds of feature extraction methods on the original response curve in a steady-state response were used, namely, baseline difference, logarithmic difference, local normalization, global normalization, and global autoscaling. The results of feature extraction were fed into a Principal Component Analysis (PCA) system. As a result, global autoscaling and normalization had the highest total sum of the first and second principal components of 96.96%, followed by local normalization (90.18%), logarithm, and baseline difference (88.92% and 79.26%, respectively). The validation of PCA results was performed using a Backpropagation Neural Network (BPNN). The highest accuracy, 97.44%, was obtained from the global autoscaling method, followed by global normalization, local normalization, logarithm, and baseline difference, with an accuracy level of 94.87%, 92.31%, 89.74%, and 82.05%, respectively. This demonstrates that the selection of the feature extraction method can affect the classification results and improve e-nose performance.
Semantic Classification of Scientific Sentence Pair Using Recurrent Neural Network Besti, Agung; Ilyas, Ridwan; Kasyidi, Fatan; Djamal, Esmeralda Contessa
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 Guntari, Ella Wahyu; Djamal, Esmeralda Contessa; Nugraha, Fikri; Liem, Sandi Lesmana
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 Puspitasari, Ayu Jati; Nicosa, Arya; Prakarsa, Dian Bayu; Harsono, Djiwo
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 Nornaim, Mohamad Hafis; Abdul-Kadir, Nurul Ashikin; Harun, Fauzan Khairi Che; Razak, Mohd Azhar Abdul
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 Permana, Aldi Sidik; Djamal, Esmeralda Contessa; Nugraha, Fikri; Kasyidi, Fatan
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 Asyrofi, Rakha; Winata, Yoni Azhar
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.

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