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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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Genetic Programming Approach for Classification Problem using GPU Leo Willyanto Santoso
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.v7.2039

Abstract

Genetic programming (GP) is a machine learning technique that is based on the evolution of computer programs using a genetic algorithm. Genetic programming have proven to be a good technique for solving data set classification problems but at high computational cost. The objectives of this research is to accelerate the execution of the classification algorithms by proposing a general model of execution in GPU of the adjustment function of the individuals of the population. The computation times of each of the phases of the evolutionary process and the operation of the model of parallel programming in GPU were studied. Genetic programming is interesting to parallelize from the perspective of evolving a population of individuals in parallel.
UFMC and f-OFDM: Contender Waveforms of 5G Wireless Communication System Ghasan Ali Hussain; Lukman Audah
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.v7.2040

Abstract

Because of the increased demand for high data rates, looking for using new technologies that meet these requirements are considered a necessary. Hence, Fifth Generation (5G) is expected to be impressive in offering these requirements and implement around 2020. Orthogonal Frequency Division Multiplexing (OFDM) is considered a main technology of LTE wireless communication standards. Due to its suffering from high Bit Error Rate (BER) and Peak Average Power Ratio (PAPR), OFDM doesn't consider as charming solution for future wireless communications and several emerging applications of 5G. Moreover, high Out of Band Emission (OOBE) and inability of supporting the flexible numerology are other demerits of OFDM systems. Thus, looking for alternative waveforms which have the ability of solving OFDM disadvantages are necessary to introduce it as contender candidate for 5G wireless communication systems. In this paper, both of Filtered-OFDM (f-OFDM) and Universal Filtered Multi carrier (UFMC) systems have been discussed for 5G wireless communication systems and compared to OFDM system. The results showed that f-OFDM system is better than both OFDM and UFMC systems and could be introducing as competitive candidate for 5G wireless communication systems because of its ability of reducing OOBE and enhancing BER performance.
Speech Recognition Implementation Using MFCC and DTW Algorithm for Home Automation Abdulloh Salahul Haq; Muhammad Nasrun; Casi Setianingsih; Muhammad Ary Murti
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.v7.2041

Abstract

The use of speech recognition as part of home automation, especially for smart homes, is an exciting thing that is still being developed. That is because of human needs for comfort, convenience, quality of life, and better safety. Speech recognition built in this study is used as a device to control smart home devices by identifying the commands spoken by users, especially in a state of clean speech. The command used is a predetermined consecutive word. For the extraction of voice commands, the MFCC algorithm is used to match spoken words with templates using the Dynamic Time Warping (DTW) algorithm. DTW algorithm can find the difference between 2-time series that have different lengths of time. The results of the accuracy of this system by using these algorithms were successfully carried out by 86.67%, with an average time required to identify the commands of 5.28 seconds.
Earthquake Early Warning System Prototype Based on Lot Using Backpropagation Algorithm Adi Pranesthi; Budhi Irawan; Casi Setianingsih
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.v7.2042

Abstract

Earthquakes are vibrations that occur on the earth's surface due to the sudden release of energy from the inside that creates seismic waves. An earthquake is caused by the movement of the earth's crust (the earth's plate). The frequency of a region refers to the type and size of earthquakes experienced during a period. Along with the development of early earthquake detection system technology provides a solution to minimize earthquake events. This research will discuss the system's design to determine the occurrence of earthquakes through time pattern analysis and Peak Ground Acceleration value. By using the Radial Basis Function Method, which later to minimize the loss of life from earthquakes. And help the main tools owned by the government. This study aims to determine the occurrence of earthquakes from Peak Ground Acceleration values and time analysis patterns, which are obtained from the decision of the Backpropagation method with an accuracy rate of 88%.
Cholesterol Detection Based on Eyelid Recognition Using Convolutional Neural Network Method Rizki Mulia Pratama; Astri Novianty; Casi Setianingsih
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.v7.2043

Abstract

Lack of public awareness of health will cause serious problems. A small example, people now tend to always consume fatty foods without thinking about the risk of cholesterol levels in the body.  Information on the level of cholesterol suffered by humans can be seen on the human eyelids. The eyelids, one part of the eye, can be known as a person's cholesterol level by observing the eyelids' shape and condition, but many people do not know about this. This application is an application made to detect cholesterol based on the shape of the eyelids. This can determine whether a person is exposed to cholesterol or not, using the Convolutional Neural Network (CNN) method in the classification process. This study provides an output in the form of early detection of cholesterol and prevention so that users can minimize the possibility of illness that will be suffered. This research was conducted to detect cholesterol one eyelid based on digital images. For detecting a cholesterol level, this system got 95.83% of accuracy.
Implementation of Linear and Lagrange Interpolation on Compression of Fibrous Peat Soil Prediction Badar Said; Faisal Estu Yulianto
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.v7.2044

Abstract

Previous studies have predicted the compression of fibrous peat soils using the Gibson & Lo method. But the prediction process is still done manually so it requires quite a long time. Therefore this research implements linear and Lagrange interpolation methods using Matlab software to speed up the prediction process. This study also carried out a comparison of the results of the implementation of the two methods to determine its effectiveness in making predictions. Based on the results of trials and analysis, it can be seen that the prediction of compression of fibrous peat soil using linear interpolation is more effective than using Lagrange interpolation, this can be proven by the smaller average RMSE prediction results using linear interpolation, with a difference in the average value of RMSE 7.7. Besides, prediction testing using Lagrange interpolation requires longer time, because it still does the iteration process as much as laboratory test data.
Characterization of Polydimethylsiloxane Dielectric Films for Capacitive ECG Bioelectrodes Alhassan Haruna Umar; Fauzan Khairi Che Harun; Yusmeeraz Yusof
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 Fladio Armandika; 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.v7.2048

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 Sanjay Kumar Sonbhadra; Sonali Agarwal; Muhammad Syafrullah; Krisna Adiyarta
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 Dyah Kurniawati Agustika; Shidiq Hidayat; Kuwat Triyana; Doina D Iliescu; Mark S Leeson
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.

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