Proceeding of the Electrical Engineering Computer Science and Informatics
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.
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<![CDATA[Applying MAC Address-Based Access Control for Securing Admin’s Login Page ]]>
<![CDATA[Bintang Maulana Prasetya Pagar Alam]]>;
<![CDATA[Rycka Septiasari]]>;
<![CDATA[Amiruddin Amiruddin]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v6.2005
<![CDATA[Authentication is a very important process for securing web applications. Username and password are two parameters commonly used for user authentication on the administrator’s login page. However, such the two authentication parameters can be easily breached so that they can become a vulnerability that adversary parties can use to conduct malicious activities. For example, the attackers can commit a crime such as data modification or theft or even more dangerous take over administrator services of a system. Therefore, it is necessary to improve the security mechanism by adding additional factor of authentication other than username and password. In this study, an improvement in authentication mechanisms was carried out by applying MAC Address-based access control as an additional authentication factor. In this method, Address Resolution Protocol (ARP) is used in mapping the users Internet Protocol (IP) address to their MAC address during validation process. The experimental results showed that the addition of the MAC address made the authentication process resistant to Dictionary Attack and Shoulder Surfing Attack.]]>
<![CDATA[Optimizing Design of Core-clad Width for Single Mode Fiber with Zero Dispersion Shift ]]>
<![CDATA[Toto Saktioto]]>;
<![CDATA[Doni Basdyo]]>;
<![CDATA[Yoli Zairmi]]>;
<![CDATA[Romi Fadli Syahputra]]>;
<![CDATA[Okfalisa Okfalisa]]>;
<![CDATA[Wresni Anggraini]]>;
<![CDATA[Syamsudhuha Syamsudhuha]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v6.2006
<![CDATA[Fiber optics have become a vital role in telecommunication technologies with many benefits, i.g. high speed transmission, non-electromagnetic interference and low energy consumption. An excellent single mode fiber (SMF) must provide a low attenuation and dispersion which occurs at same wavelength, i.e. 1550 nm. But, in silica based SMF, this property cannot be achieved in a bulk form. Meanwhile, the direct experiment is really not the best choice. Therefore, a simulation fiber design take a crucial role into account for obtaining zero dispersion shift. We design SMF geometry with zero dispersion by resizing the width of core and cladding. This design consists of inner and outer core-clad profile. We also provide several width boundaries for matching the lowest dispersion to the lowest attenuation in silica fiber-based. Moreover, the results shows that dispersion property of the design is suitable for long-haul optical communication systems.]]>
<![CDATA[E-Commerce Delivery Order System Based On ISO 9126 Model In Jeddah, Saudi Arabia ]]>
<![CDATA[Siswanto Siswanto]]>;
<![CDATA[H. Riefky Sungkar]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v6.2007
<![CDATA[The limited mobility of Muslim women in the city of Jeddah who must be accompanied by their Muslim family or husband or with fellow Muslim female friends, if they want to leave the house to shop or entrepreneurship has become a culture in the country of Saudi Arabia. The research objective was to create a prototype e-commerce delivery order system for Muslim women in the city of Jeddah. The development of an ecommerce delivery order system uses a prototype method, and tests the quality of variables with the ISO 9126 model. The result of testing of the application variables for functionality, reliability, efficiency and user usability is 77.3%.]]>
<![CDATA[Deep Learning Approaches for Big Data Analysis ]]>
<![CDATA[Naomie Salim]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v6.2008
<![CDATA[Good representations of data eliminate irrelevant variability of the input data, while preserving the information that is useful for the ultimate task. Among the various ways for learning representation is using deep learning methods. Deep feature hierarchies are formed by stacking unsupervised modules on top of each other, forming multiple non-linear transformations to produce better representations. In this talk, we will first show how deep learning is used for bioactivity prediction of chemical compounds. Molecules are represented as several convolutional neural networks to predict their bioactivity. In addition, a new concept of merging multiple convolutional neural networks and an automatic learning features representation for the chemical compounds was proposed using the values within neurons of the last layer of the CNN architecture. We will also show how the concepts of deep learning is adapted into a deep belief network (DBN) to enhance the molecular similarity searching. The DBN achieves feature abstraction by reconstruction weight for each feature and minimizing the reconstruction error over the whole feature set. The DBN is later enhanced using data fusion to obtain a lower detection error probability and a higher reliability by using data from multiple distributed descriptors. Secondly, we will show how we used deep learning for stock market prediction. Here, we developed a Deep Long Short Term Memory Network model that is able to forecast the crude palm oil price movement with combined factors such as other commodities prices, weather and news sentiments and price movement of crude palm oil. We will also show how we combined stock markets price and financial news and deployed the Long Short Term Memory (LSTM), Recurrent Neural Network (RNN), and Word 2 Vector (Word2Vec) to project the stock prices for the following seven days. Finally, we will show how we exploited deep learning method for the opinion mining and later used it to extract the product's aspects from the user textual review for recommendation systems. Specifically, we employ a multichannel convolutional neural network (MCNN) for two different input layers, namely, word embedding layer and Part-of-speech (POS) tag embedding layer. We will show effectiveness of the proposed model in terms of both aspect extraction and rating prediction performance.]]>
<![CDATA[MAC for Internet of Things (IoT) ]]>
<![CDATA[Shekhar Verma]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v6.2009
<![CDATA[Internet of Things (IoT) networks are expected to consist of a large number of resource constrained devices that gather data by sensing their environment and communicate dynamically with access points or neighboring devices to communicate these small amount of location specific delay-sensitive data. A IoT MAC protocol must be able to support the high-intensity and short-lived demands of these IoT networks. The basic design questions to be addressed are, one, why endure a high-overhead and large-delay MAC protocol in IoT networks when only a few intermittent packets need to be sent and received? Two, how to ensure energy efficiency even when energy harvesting is available? Three, what kind of access technique should be employed; grant based or grant free? In this talk, we take a look at how existing wireless MAC protocols are being adapted to cater to the specific needs of IoT networks which is imperative to address the basic design questions. Recent research proposals for IoT MAC protocols that endeavor to address the needs shall also be examined for their efficacy and promise.]]>
<![CDATA[A Real-Time Visible Light Communication System on Chip Design for High Speed Wireless Communication ]]>
<![CDATA[Trio Adiono]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v6.2010
<![CDATA[The increasing demand of wireless communication bandwidth due to advancement of IoT and smartphone technology, requires the new wireless communication technology that can provide high speed wireless communication. The Visible Light Communication (VLC) has been proven can provide multi gigabit wireless communication throughput using unlicensed visible light spectrum. Therefore, VLC is a promising technology to solve bandwidth limitation problem. In order to achieve high speed throughput, VLC signal processing has to be implemented using Application Specific Integrated Circuits (ASICs) technology. In this research, we develop a baseband processor architecture for VLC application. We use System on Chip (SoC) design approach to reduce design time and easy system integration to various applications. In order to increase spectrum efficiency, we utilize OFDM modulation scheme. Several OFDM processing blocks, such as synchronizer, FFT/IFFT, modulator, demodulator, are designed in the system. The real-time system performance is verified in FPGA based system prototyping. The design includes optical wireless front end module, baseband processing and network layer. The developed prototype shows a real-time performance for high speed internet access.]]>
<![CDATA[Implementation of Image Segmentation Techniques to Detect MRI Glioma Tumour ]]>
<![CDATA[Siti Rafidah Binti Kassim]]>;
<![CDATA[Setyawan Widyartoh]]>;
<![CDATA[Mohammad Syafrullah]]>;
<![CDATA[Krisna Adiyarta]]>;
<![CDATA[Widya Kumala Sari]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v6.2011
<![CDATA[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.]]>
<![CDATA[Left Ventricle Heart Three Dimension Mechanical Simulation for Kinetic Energy ]]>
<![CDATA[Mohd Hafizulhadi Mohd Asri]]>;
<![CDATA[Muhammad Haikal Satria]]>;
<![CDATA[Arief Marwanto]]>;
<![CDATA[M. Haider Abu Yazid]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v6.2012
<![CDATA[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.]]>
<![CDATA[Detection of EEG Signal Post-Stroke Using FFT and Convolutional Neural Network ]]>
<![CDATA[Esmeralda C. Djamal]]>;
<![CDATA[Widiyanti Isni Furi]]>;
<![CDATA[Fikri Nugraha]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v6.2013
<![CDATA[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).]]>
<![CDATA[Comparison of EEG Pattern Recognition of Motor Imagery for Finger Movement Classification ]]>
<![CDATA[Khairul Anam]]>;
<![CDATA[Mohammad Nuh]]>;
<![CDATA[Adel Al-Jumaily]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v6.2014
<![CDATA[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.]]>
