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[Classification of Motor Imagery and Synchronization of Post-Stroke Patient EEG Signal ]]>
<![CDATA[Arifah Ummul Fadiyah]]>;
<![CDATA[Esmeralda C. Djamal]]>
<![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.v6.1935
<![CDATA[Stroke attacks often cause disability, so the need for rehabilitation to restore patient's motor skills. Electroencephalogram (EEG) is an instrument that can capture electrical activity in the brain. Some post-stroke patients have brain electrical dysfunction so that EEG signal can achieve such as amplitude decrease, and wave differences from symmetric channels. However, EEG signal analysis is not easy because it has high complexity and small amplitude. However, information from EEG signals is beneficial, including for stroke identification. This study proposes the identification of EEG signals from post-stroke patients using wavelet extraction and Backpropagation Levernberg-Marquardt. EEG signals are recorded, extracted imagery motor variables, and synchronization of symmetric channels. The results of the study provide that the accuracy for identifying post-stroke EEG signals is 100% for training data and 79.69 % for new data. Research also shows that the use of learning rates affects accuracy. The smaller the learning rate provided accuracy is better. However, it had consequences for computing time so that the optimal learning rate is 0.0001.]]>
<![CDATA[Early Detection Application of Bipolar Disorders Using Backpropagation Algorithm ]]>
<![CDATA[Desti Fitriati]]>;
<![CDATA[Febri Maspiyanti]]>;
<![CDATA[Fairuz Astari Devianty]]>
<![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.v6.1936
<![CDATA[Mental health is an important aspect in realizing overall health and important to be considered as physical health. Mental disorders are classified as difficult to diagnose due to the similarity of symptoms that can occur. In addition, information about mental disorders is inadequate so that it can be difficult for experts to provide a diagnosis of the disorders experienced by patients. The difficulty of experts in diagnosing is usually caused by the similarity of symptoms in mental disorders, such as in schizophrenia and bipolar disorder. Based on these problems, this research would like to conduct an early detection study of bipolar disorder by using screening questionnaire data from 300 respondents and serve as a knowledge base to be processed using the backpropagation algorithm. Based on all the results of testing the backpropagation algorithm that has been done to find out the results obtained accuracy and the highest results of training, the highest results obtained with the total test data correct or suitable is 249 and the wrong data is 1 of 250 test data. If it is calculated by a formula, the resulting accuracy rate is 99.6%. And it can be concluded broadly that the greatest influence of the accuracy of the backpropagation algorithm is based on momentum. Because in testing momentum the highest accuracy can be produced compared to the results of other analyzes.]]>
<![CDATA[The Kinematics and Dynamics Motion Analysis of a Spherical Robot ]]>
<![CDATA[Tresna Dewi]]>;
<![CDATA[Pola Risma]]>;
<![CDATA[Yurni Oktarina]]>;
<![CDATA[Lin Prasetyani]]>;
<![CDATA[Zarqa Mulya]]>
<![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.v6.1937
<![CDATA[Mobile robot application has reach more aspect of life in industry and domestic. One of the mobile robot types is a spherical robot whose components are shielded inside a rigid cell. The spherical robot is an interesting type of robot that combined the concept of a mobile robot and inverted pendulum for inner mechanism. This combination adds to more complex controllerdesignthantheothertypeofmobilerobots.Asidefrom these challenges, the application of a spherical robot is extensive, from being a simple toy, to become an industrial surveillance robot. This paper discusses the mathematical analysis of the kinematics and dynamics motion analysis of a spherical robot. The analysis combines mobile robot and pendulum modeling as the robot motion generated by a pendulum mechanism. This paper is expected to give a complete discussion of the kinematics and dynamics motion analysis of a spherical robot.]]>
<![CDATA[The Improved Artificial Neural Network Based on Cosine Similarity in Facial Emotion Recognition ]]>
<![CDATA[Kartika Candra Kirana]]>;
<![CDATA[Slamet Wibawanto]]>;
<![CDATA[Nur Hidayah]]>;
<![CDATA[Gigih Prasetyo Cahyono]]>
<![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.v6.1938
<![CDATA[In this study, we present the improved artificial neural network based on cosine similarity in facial emotion recognition. We apply a shifting window that employs neural network for two concurrent processes consisting of face detection and emotional recognition. In order to prevent the slow and futile computations, non-face areas need to be filtered from neurons on each network layer, thus we propose the improved artificial neural network based on cosine similarity. Cosine similarity is employed to bypass the process of non-face areas in neural network. The accuracy of the proposed method reaches 0.84, while the accuracy of the original neural network method reaches 0.74. It can be concluded that our methods work accurately.proposed method is superior to the state-of-the-art algorithms.]]>
<![CDATA[Emotion and Attention of Neuromarketing Using Wavelet and Recurrent Neural Networks ]]>
<![CDATA[Muhammad Fauzan Ar Rasyid]]>;
<![CDATA[Esmeralda C. Djamal]]>
<![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.v6.1939
<![CDATA[One method concerning evaluating video ads is neuromarketing. This information comes from the viewer's mind, thus minimizing subjectivity. Besides, neuromarketing can overcome the difficulties of respondents who sometimes do not know the response to the video ads they watch. Neuromarketing is based on neuropsychology, which is sourced from the human brain through electrical activity signals recorded by Electroencephalogram. Usually, Neuropsychology consists of emotions, attention, and concentration. This research proposed the Wavelet method and Recurrent Neural Networks to measure the emotional and attention variable of neuropsychology in real-time every two seconds while watching video ads. The results showed that Wavelet and Recurrent Neural Networks could provide training data accuracy of 100% and 89.73% for new data. The experiment also gave that the RMSprop optimization model for the weight correction contributed to higher correctness of 1.34% than the Adam model. Meanwhile, using Wavelet for extraction can increase accuracy by 4%.]]>
<![CDATA[An SoC-Based System for Real-time Contactless Measurement of Human Vital Signs and Soft Biometrics ]]>
<![CDATA[Aminuddin Rizal]]>;
<![CDATA[Kuan-Ting Chiang]]>;
<![CDATA[Jia-Wei Lin]]>;
<![CDATA[Yuan-Hsiang Lin]]>
<![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.v6.1940
<![CDATA[Computer vision (CV) plays big role in our current society's life style. The advancement of CV technology brings the capability to sense human vital sign and soft biometric parameters in contactless way. In this work, we design and implement the contactless human vital sign parameters measurement including pulse rate (PR) and respiration rate (RR) and also for assessment of human soft biometric parameters i.e. age, gender, skin color type, and body height. Our designed system is based on system on chip (SoC) device which run both FPGA and hard processor while provides real-time operation and small form factor. Experimental results shows our device performance has mean absolute error (MAE) 2.85 and 1.46 bpm for PR and RR respectively compared to clinical apparatus. While, for soft biometric parameters measurement we got unsatisfied results on age and gender estimation with accuracy of 58% and 74% respectively. However, for skin color type and body height measurement we reach high accuracy with 98 % and 2.28 cm respectively on both parameters.]]>
<![CDATA[Optical Studies of Er-doped Yttrium Aluminium Garnet Phosphor Materials ]]>
<![CDATA[N. Norhashim]]>;
<![CDATA[S. Kaveh]]>;
<![CDATA[A. K. Cheetham]]>;
<![CDATA[R. J. Curry]]>
<![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.v6.1941
<![CDATA[The need for materials application in solid-state lasers, medical devices, and optoelectronic devices has made the investigation of ceramic materials of increasing importance. A detail study of the optical properties of rare earth element typically from luminescent materials when intentionally doped inside the host materials and in particular crystal (such as YAG) is reported for the photoluminescence, power and lifetime measurement. The rare-earth dopants usually form trivalent lanthanide ions and the energy transfer and optical transitions involved originate from 4f-4f transitions of the ions and between these states and the host material. In order to understand the energy transfer processes in more detail we need to better understand the accompanying optical processes that give rise to the emission they display and it is this that forms the focus of the work presented. Following this second (and higher) order processes are considered that lead to upconversion in erbium-doped yttrium aluminum garnet (Er:YAG) materials.]]>
<![CDATA[Flatbuffers Implementation on MQTT Publish/Subscribe Communication as Data Delivery Format ]]>
<![CDATA[Muhammad Adna Pradana]]>;
<![CDATA[Andrian Rakhmatsyah]]>;
<![CDATA[Aulia Arif Wardana]]>
<![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.v6.1942
<![CDATA[Communication between devices can be done in various ways, one of them is the Publish / Subscribe model that uses the MQTT protocol From the shortcomings that exist in JSON, such as long processing time, Google recently introduced a new data format called Flatbuffers. Flatbuffers has a better data format serialization process than other data formats. This paper will discuss the implementation and testing of the Flatbuffers data format performance compared to other data formats through the MQTT Publish / Subscribe communication model. Testing is done by measuring the value of payload, latency, and throughput obtained from each data format. The test results show that the Flatbuffers data format is very well used as a data extraction format based on data processing latency of 0.5002 ms and throughput 518.4649 bytes/ms with payload 0.996108949 character/byte.]]>
<![CDATA[Classification of Physiological Signals for Emotion Recognition using IoT ]]>
<![CDATA[Sadhana Tiwari]]>;
<![CDATA[Sonali Agarwal]]>;
<![CDATA[Muhammad Syafrullah]]>;
<![CDATA[Krisna Adiyarta]]>
<![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.v6.1943
<![CDATA[Emotion recognition gains huge popularity now a days. Physiological signals provides an appropriate way to detect human emotion with the help of IoT. In this paper, a novel system is proposed which is capable of determining the emotional status using physiological parameters, including design specification and software implementation of the system. This system may have a vivid use in medicine (especially for emotionally challenged people), smart home etc. Various Physiological parameters to be measured includes, heart rate (HR), galvanic skin response (GSR), skin temperature etc. To construct the proposed system the measured physiological parameters were feed to the neural networks which further classify the data in various emotional states, mainly in anger, happy, sad, joy. This work recognized the correlation between human emotions and change in physiological parameters with respect to their emotion.]]>
<![CDATA[Diagnosis of Smear-Negative Pulmonary Tuberculosis using Ensemble Method: A Preliminary Research ]]>
<![CDATA[Rusdah Rusdah]]>;
<![CDATA[Mohammad Syafrullah]]>
<![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.v6.1944
<![CDATA[Indonesia is one of 22 countries with the highest burden of Tuberculosis in the world. According to WHO’s 2015 report, Indonesia was estimated to have one million new tuberculosis (TB) cases per year. Unfortunately, only one-third of new TB cases are detected. Diagnosis of TB is difficult, especially in the case of smear-negative pulmonary tuberculosis (SNPT). The SNPT is diagnosed by TB trained doctors based on physical and laboratory examinations. This study is preliminary research that aims to determine the ensemble method with the highest level of accuracy in the diagnosis model of SNPT. This model is expected to be a reference in the development of the diagnosis of new pulmonary tuberculosis cases using input in the form of symptoms and physical examination in accordance with the guidelines for tuberculosis management in Indonesia. The proposed SNPT diagnosis model can be used as a cost-effective tool in conditions of limited resources. Data were obtained from medical records of tuberculosis patients from the Jakarta Respiratory Center. The results show that the Random Forest has the best accuracy, which is 90.59%, then Adaboost of 90.54% and Bagging of 86.91%.]]>
