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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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Implementation of Image Segmentation Techniques to Detect MRI Glioma Tumour Siti Rafidah Binti Kassim; Setyawan Widyartoh; Mohammad Syafrullah; Krisna Adiyarta; Widya Kumala Sari
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.2011

Abstract

Image identification to detect a tumour needs several stages of image processing along with identifying analysis. To get an accurate segmentation of the tumour contour and to identify brain tumour based on brain magnetic resonance imaging (MRI), a suitable techniques and stages of image processing are required to be applied. One technique of mid-level image processing became an objective this work. The objective of the study is to segment the boundary of tumour by applying the Modification of Region Fitting (MRF) method in term of data fitting. The performance of the Region Scalable Fitting (RSF) method and Modified Region Scalable Fitting (MRSF) is evaluated by comparing the number of iterations. As the result, the MRF method has successfully segmented the initial region of braintumour images.
Left Ventricle Heart Three Dimension Mechanical Simulation for Kinetic Energy Mohd Hafizulhadi Mohd Asri; Muhammad Haikal Satria; Arief Marwanto; M. Haider Abu Yazid
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.2012

Abstract

The major drawbacks of current pacemaker are the battery replacement. Patient will need additional surgery to replace the pacemaker unit with the new one. It has been suggested to use rechargeable battery to solve this issue. Recharging a battery within the body, however, is not viable owing to the lifetime of tissue heating and battery charging. For these purposes, the use of piezo-polymer is appropriate as a power harvester for a self-powered pacemaker. Piezo-polymer was commonly used for energy harvesting, but none for implantable cardiothoracic devices. This study focuses on identifying the optimum location on the heart to put the piezo-polymer. This research is conducted by simulation of left ventricle of heart via ANSYS. Heart stress-strain Finite Element Analysis (FEA) are employed to obtain the maximum harvested power. The result shows the location of myocardial contraction that produces sufficient kinetic energy for the placement of the pacemaker. The heart 3-dimensional images are taken from cardiac-CT or cardiac-MRI to search the optimum location on the heart for energy harvesting and minimize pacing energy. Left ventricle electronics model is created to represent the movement of the left ventricle and how piezo-polymer works. In conclusion, the left ventricular wall movement and deformation induced by the movement of the cardiac wall were analyzed in the simulation using the left ventricular model to obtain the place of the peak kinetic energy.
Detection of EEG Signal Post-Stroke Using FFT and Convolutional Neural Network Esmeralda C. Djamal; Widiyanti Isni Furi; Fikri Nugraha
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.2013

Abstract

Stroke is a condition that occurs when the blood supply to the brain is disrupted or reduced. It may be caused by a blockage (ischemic stroke) or rupture of a blood vessel (hemorrhagic stroke) so that it can cause disability. Therefore patients need to undergo rehabilitation. One of the procedures of monitoring of the recovery of stroke patients using the National Institutes of Health Stroke Scale (NIHSS) method, but sometimes subjectively. Electroencephalogram (EEG) is an instrument that can measure electrical activity in the brain, including abnormalities caused by stroke. This study investigates EEG signal detection in post-stroke patients using Fast Fourier Transform (FFT) and 1D Convolutional Neural Network (1D CNN). Fast Fourier Transform (FFT) extraction can increase accuracy from 60% to 80.3% from the use of Adam's optimization model. Meanwhile, the AdaDelta model gave 20% accuracy without FFT. And its condition increased to 79.9% with FFT extraction. Therefore, Adam's stability has the advantage of remembering to use hyper-parameter. On the other hand, FFT is beneficial for directing information used for the use of 1D CNN, thus increasing accuracy. The results showed that using of Fast Fourier Transform (FFT) in identification could increase accuracy by 45-80% compared to identification using only 1D CNN. Meanwhile, the results of the study show that the relative weight correction model using Adaptive Moment Estimation (Adam) provided higher accuracy compared to the Adaptive learning rate (AdaDelta).
Comparison of EEG Pattern Recognition of Motor Imagery for Finger Movement Classification Khairul Anam; Mohammad Nuh; Adel Al-Jumaily
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.2014

Abstract

The detection of a hand movement beforehand can be a beneficent tool to control a prosthetic hand for upper extremity rehabilitation. To be able to achieve smooth control, the intention detection is acquired from the human body, especially from brain signal or electroencephalogram (EEG) signal. However, many constraints hamper the development of this brain-computer interface (BCI, especially for finger movement detection). Most of the researchers have focused on the detection of the left and right-hand movement. This article presents the comparison of various pattern recognition method for recognizing five individual finger movements, i.e., the thumb, index, middle, ring, and pinky finger movements. The EEG pattern recognition utilized common spatial pattern (CSP) for feature extraction. As for the classifier, four classifiers, i.e., random forest (RF), support vector machine (SVM), k-nearest neighborhood (kNN), and linear discriminant analysis (LDA) were tested and compared to each other. The experimental results indicated that the EEG pattern recognition with RF achieved the best accuracy of about 54%. Other published publication reported that the classification of the individual finger movement is still challenging and need more efforts to make the best performance.
SeizeIT: SEIZURE victims are no longer leashed M.A.J.I. Wimalarathne; K.U.K. Ubeysingha; I.A.D.M. Imbulana; W.A.D.R. Welikala; Koliya Pulasinghe
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.2015

Abstract

Seizure considered to be one of the severe and most common type of neurological disorders. Despite the availability of numerous anti-seizure drugs, it is often difficult to control the disease completely and effectively. Lack of close supervision and failure in providing urgent medical care during and after seizure episodes, leads to serious injuries or even death. On the other hand, Use of wireless sensor networks in everyday applications have rapidly increased due to decreased technology costs and improved product reliability. Therefore developing a wearable device to monitor seizure may complete the anamnesis, help medical staff in diagnosing and acute treatment while preventing seizure related accidents. There are number of seizure detection systems available in the market. Still their performance is far from perfect. This paper explores an application of biomedical wireless sensor networks, which attempts to monitor patients in a completely non-invasive and non-intrusive manner. It describes a wearable device together with seizure prediction and alerting system, which is designed to address some issues with seizure detection systems in the market. Its functional block diagram and operating modes are detailed. Possible application areas of the device are also discussed
Controlled Position Navigation of Single Degree Magnetic Levitation Dhiraj Basnet; Anusha Lamichhane; Amrit Panthi; Bipin Lamichhane; Mahammad Badrudoza; Ram Prasad Pandey
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.2016

Abstract

A permanent magnet is levitated following the electromagnetic suspension principle using the attractive magnetic force of a wire-wound electromagnet with a hall-effect sensor for position feedback. Taking the hall-effect voltage as an analog parameter and feedback signal to the micro-controller, the strength of the electromagnet is controlled by adjusting the current using the Pulse Width Modulation technique in order to levitate the permanent magnet. The stability of the levitated magnet is enhanced by the use of PID algorithm in the embedded system. Use of Laplace transform for simplification of differential equations and Taylor series for the linearization of system function supports the mathematical computation required for the levitation. Furthermore, by making the feedback signal from hall-effect sensor dependent only on the magnetic field of levitating magnet, an advancement in levitation phenomenon is achieved that aids the levitation with a greater flexibility of changing the position of the levitating magnet along the gravitational axis within a specified range.So the paper depicts about the "Controlled Position Navigation of Single Degree Magnetic Levitation".
Performance improvement of MO surge arrester using high gradient arrester block against VFTOs Kannadasan Raju
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.2017

Abstract

It is well known that the metal oxide surge arrester is inoperative against very fast transients overvoltages (VFTOs) because of its strong stray capacitive effect. This stray effect causes a time lag between the peak of residual voltage and peak of the current surge and so there is a delay its response. In order to reduce the stray effect, high gradient material is used for preparing metal oxide arrester blocks with different compositions. For simulation study, the required electrical parameters of high gradient arrester blocks are calculated with estimated height of arrester. This model is simulated using Electromagnetic transient program (EMTP) for different arrester ratings against switching, lightning, steep and very fast transients. The simulated value of residual voltages are compared with experimental values. From the observed results, it is perceived that the newly developed high gradient arrester decreases the delay and so the dynamic performance of the arrester is improved especially against very fast transients.
Determination of Appropriate Overhead Line Insulator in Sumatra due to Contamination Severity Arpan Zaeni; Umar Khayam; Deny Viviantoro
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.2018

Abstract

Insulator is one of the important equipment to support electrical power delivery which flow through the transmission line. Considering its very important role, the selection of insulators must be certainly based on deep analysis so that the insulator we choose works properly. There are several standards that can be used in selecting isolators, but in this paper the standards that will be used for case study analysis are IEC and IEEE standards. Case studies that will be used for the selection of insulators are for Sumatra that located in Indonesia which is a tropical country and certainly has special environmental characteristics that can influence the selection parameters of an insulator. There are several parameters that are commonly used in selecting overhead isolators those are power frequency voltage, environmental condition (contamination), switching over voltage, and lightning over voltage. Using environmental condition, it is found that the pollution category of Sumatra area is heavy, which influence the selection of insulation material.
The Feasibility of Credit Using C4.5 Algorithm Based on Particle Swarm Optimization Prediction Siswanto Siswanto; Abdussomad Abdussomad; Windu Gata; Nia Kusuma Wardhani; Grace Gata; Basuki Hari Prasetyo
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.2019

Abstract

Credit is a belief that one is given to a person or other entity which is concerned in the future will fulfill all the obligations previously agreed. The objective of research is necessary to do credit analysis to determine the feasibility of a credit crunch, through credit analysis results, it can be seen whether the customer is feasible or not. The methods are is used to predict credit worthiness is by using two models, models classification algorithm C4.5 and C4.5 classification algorithm model based Particle Swarm Optimization (PSO). After testing with these two models found that the result C4.5 classification algorithm generates a value of 90.99% accuracy and AUC value of 0.911 to the level diagnostics Classification Excellent, but after the optimization with C4.5 classification algorithm based on Particle Swarm Optimization accuracy values amounted to 91.18% and the AUC value of 0.913 to the level of diagnosis Excellent Classification. These both methods have different accuracy level of 0.18%.
Data Mining Implementation to Predict Sales Using Time Series Method Agung Triayudi; Sumiati Sumiati; Thoha Nurhadiyan; Vidila Rosalina
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 2: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2028

Abstract

Sales transaction data histories can be used to predict the possibility of sales transaction that will occur in the future. These characteristics are in accordance with forecasting using time series method where this method uses previous data as tools to predict transaction value that will appear in the present time. Company X that runs its business by sell their product through distributors has sales data that is not optimally utilized. The average number of sales per year ranges from 5000 transactions which is not use to forecast transactions hereafter. Transaction data is stored in the company database so that data mining technology can be applied to support company X transaction data collection from previous year. The data is processed in applications where the results of forecasting are compared with real data in 2018 to see the accuracy of the forecasting results. The graphic that shown in application has pattern which can use for forecasting. From the forecasting method used, it can be seen that the forecasting results show data that came out did not produce data that matched the real data where the highest level of accuracy was 99.68% and the lowest accuracy was still above 50%.

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