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Proceeding of the Electrical Engineering Computer Science and Informatics

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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.

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Articles 144 Documents

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Search results for , issue "Vol 5: EECSI 2018" : 144 Documents clear

Robust Adaptive Sliding Mode Control Design with Genetic Algorithm for Brushless DC Motor Een Hutama Putra; Zulfatman Has; Machmud Effendy
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

This study aims to design a control scheme that is capable to improve performance and efficiency of brushless DC motor (BLDC) in operating condition. The control scheme is composed of sliding mode controller (SMC) with proportional-integral-derivative (PID) sliding surface. The PID sliding surface is used to improve the system transient response. Then, the SMC-PID is optimized by genetic algorithm optimization for further improvement on the stability and robustness against nonlinearities and disturbances. Chattering problem that appear in the SMC is minimized by employing an adaptive switching gain for the SMC that is integrated with Luenberger Observer. Lyapunov function candidate is applied to guarantee the stability of the system. Simulation on the proposed work is done in Matlab Simulink. Results of the simulation works indicate that the proposed control scheme can improve the transient response, the stability and robustness of the BLDC motor compared to the conventional SMC in the existence of nonlinearities and disturbances.

Sensorless PMSM Control using Fifth Order EKF in Electric Vehicle Application Nanda Avianto Wicaksono; Bernadeta Wuri Harini; Feri Yusivar
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

This paper is intended to design a controller and an observer of a sensorless PMSM (permanent magnet synchronous motor) in electric vehicle application. The controller uses the field orientation control (FOC) method and the observer type is the fifth order extended Kalman filter (EKF). The designed controller and observer are tested by varying the elevation angle of the route that is several times abruptly changed. The simulation result shows that the designed controller and observer can respond to the elevation angles given.

Emotion Recognition using Fisher Face-based Viola-Jones Algorithm Kartika Candra Kirana; Slamet Wibawanto; Heru Wahyu Herwanto
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

In the form of the image integral, this primitive feature accelerates the performance of the Viola-Jones algorithm. However, the robust feature is necessary to optimize the results of emotion recognition. Previous research [11] has shown that fisher face optimized projection matrix in the low dimensional features. This feature reduction approach is expected to balance time-consuming and accuracy. Thus we proposed emotion recognition using fisher face-based Viola-Jones Algorithm. In this study, PCA and LDA are extracted to get the fisher face value. Then fisher face is filtered using Cascading AdaBoost algorithm to obtain face area. In the facial area, the Cascading AdaBoost algorithm re-employed to recognize emotions. We compared the performance of the original viola jones and fisher face-based viola jones using 50 images on the State University of Malang dataset by measuring the accuracy and time-consuming in the fps. The accuracy and time-consuming of the Viola-Jones algorithm reach 0.78 and 15 fps, whereas our proposed methods reach 0.82 and 1 fps. It can conclude that the fisher face-based viola-jones algorithm recognizes facial emotion as more accurate than the viola-jones algorithm.

Sizing Optimization and Operational Strategy of Hres (PV-WT) using Differential Evolution Algorithm Ilham Pakaya; Zulfatman Has; Annas Alif Putra
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

The instability of energy resources and corresponding cost of the system are the main two problems for designing the hybrid solar-wind power generation systems. The configuration of the system must have a high reliability on the power supply availability but with a minimum cost. The purpose of this paper is to find the most optimum or balanced configuration between technical reliability and total annual cost for the PV module number, the wind turbine number, and the battery number. The appropriate strategy of load management is needed by adjusting the potential energy resource to the load power demand. Loss of Power Supply Probability (LPSP) is a method to determine the ratio of power generation unavailability by the system configuration which used as technical analysis. Annualized Cost of System (ACS) is a method to determine the total annualized cost of the project lifetime which used as economic analysis. The result from the simulation showed that the Differential Evolution (DE) algorithm can be an alternative method to find the best configuration with a low number of LPSP and ACS. Since DE has a better efficacy and faster time to find global optimum than other algorithms.

ISO/IEC 9126 Quality Model for Evaluation of Student Academic Portal Edy Budiman; Masna Wati; Joan Angelina Widians; Novianti Puspitasari; Muhammad Firdaus; Faza Alameka
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

The papers discuss and evaluate the quality of student academic portal using ISO / IEC 9126 quality modeling approach. Quality factors are tested and analyzed are characteristics of Usability, Reliability, Efficiency, and Portability, this evaluation is very important, considering the number of users of the portal system so much and growing. one of the efforts to improve and optimize the performance of the academic information system management team. Evaluate the quality of the user perception approach 10 principles of usability heuristics and internal site performance testing or web server. The results of analysis and testing of 4 quality characteristics have issued several recommendations for improvement and optimization of the performance of student academic portals.

Measurement of IS/IT Investment on the Implementation of ERP and the Effect on company productivity Qilbaaini Effendi Muftikhali; Apol Pribadi
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Information technology can not be denied in daily activities and is the source of life of some business processes that cause companies to compete in making IT investments. Noted that IT investment increased significantly. However, the data also indicated this investment is not always followed by the achievement of organizational performance. This is known as IT Productivity Paradox, where the benefits obtained do not match what is invested. This phenomenon has long been discussed to this day. IT Productivity Paradox has become an interesting topic in some circles because some findings find different results. Some research proves that investment in IT / IS has driven the performance of the company, while not at other companies. This study aims to determine the phenomenon of IT productivity. The object of research is an organization that has invested and developed ERP system for the last 2 years. The analysis using Information Economics method. The result of investment measurement from this research shows that the application of ERP using Economic Information method in the period of about 2 years shows total project score (64,6) with predicate of influential project. The total score of the project is derived from three aspects of benefits, namely the real aspect, the quasi-real aspect, and the intangible aspect.

IDEnet : Inception-Based Deep Convolutional Neural Network for Crowd Counting Estimation Samuel Cahyawijaya; Bryan Wilie; Widyawardana Adiprawita
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

In crowd counting task, our goals are to estimate density map and count of people from the given crowd image. From our analysis, there are two major problems that need to be solved in the crowd counting task, which are scale invariant problem and inhomogeneous density problem. Many methods have been developed to tackle these problems by designing a dense aware model, scale adaptive model, etc. Our approach is derived from scale invariant problem and inhomogeneous density problem and we propose a dense aware inception based neural network in order to tackle both problems. We introduce our novel inception based crowd counting model called Inception Dense Estimator network (IDEnet). Our IDEnet is divided into 2 modules, which are Inception Dense Block (IDB) and Dense Evaluator Unit (DEU). Some variations of IDEnet are evaluated and analysed in order to find out the best model. We evaluate our best model on UCF50 and ShanghaiTech dataset. Our IDEnet outperforms the current state-of-the-art method in ShanghaiTech part B dataset. We conclude our work with 6 key conclusions based on our experiments and error analysis.

Active Fault Tolerance Control For Sensor Fault Problem in Wind Turbine Using SMO with LMI Approach Nuralif Mardiyah; Novendra Setyawan; Bella Retno; Zulfatman Has
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

In this paper, we start to investigate the sensor fault problem in a Wind Turbine model with Fault Tolerant Control (FTC). FTC is used to allow the parameters of the controller to be reconfigured in accordance error information obtained online from sensors to improve the stability and overall performance of the system when an error occurs. The design is divided into two parts. The first part is designed Sliding Mode Observer (SMO) based Fault Detection Filter (FDF) to generate a residual signal to estimate fault. FDF is designed to maximize sensitivity fault. The second is a design output feedback control and Fault Compensation to guarantee the stability and performance system from disturbance by ignoring faults. Moreover, the function of fault compensation is to minimize effect fault of the system. The main contribution of this research is FTC proved to solve the sensor fault problem in a Wind Turbine model. The simulation showed the effectiveness of this method to estimate the fault and stabilized the system faster to a steady condition.

Analysis on Customer Satisfaction Dimensions in P2P Accommodation using LDA: A Case Study of Airbnb Kevin Situmorang; Achmad Hidayanto; Alfan Wicaksono; Arlisa Yuliawati
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Customer satisfaction becomes a key influencer for people's habits or daily activities. One of the examples is in the decision-making process about whether they will use specific products or services. People often need other's review or rating about what they are going to use or consume. In this research, by using customer's online review that available from Airbnb website, we try to extract what are the most talked factors about peer-to-peer accommodation, and how customer sentiment about them. We use Latent Dirichlet Allocation (LDA) to extract that factors and conduct sentiment analysis by utilizing semantic analyzer from Google Cloud NLP. We analyze which factors that has more effect on customer satisfaction, not only in general but more specific based on customer gender and tourism destination object. The result shows that factors related to social benefit and service quality have impact on customer satisfaction, moreover different customer gender and different tourism object destination bring different sentiment among customer. We also find several factors that can be improved by the owner of the accommodation to improve customer satisfaction toward their services.

Multispectral Imaging and Convolutional Neural Network for Photosynthetic Pigments Prediction Kestrilia Prilianti; Ivan C. Onggara; Marcelinus A.S. Adhiwibawa; Tatas H.P. Brotosudarmo; Syaiful Anam; Agus Suryanto
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

The evaluation of photosynthetic pigments composition is an essential task in agricultural studies. This is due to the fact that pigments composition could well represent the plant characteristics such as age and varieties. It could also describe the plant conditions, for example, nutrient deficiency, senescence, and responses under stress. Pigment role as light absorber makes it visually colorful. This colorful appearance provides benefits to the researcher on conducting a nondestructive analysis through a plant color digital image. In this research, a multispectral digital image was used to analyze three main photosynthetic pigments, i.e., chlorophyll, carotenoid, and anthocyanin in a plant leaf. Moreover, Convolutional Neural Network (CNN) model was developed to deliver a real-time analysis system. Input of the system is a plant leaf multispectral digital image, and the output is a content prediction of the pigments. It is proven that the CNN model could well recognize the relationship pattern between leaf digital image and pigments content. The best CNN architecture was found on ShallowNet model using Adaptive Moment Estimation (Adam) optimizer, batch size 30 and trained with 15 epoch. It performs satisfying prediction with MSE 0.0037 for in sample and 0.0060 for out sample prediction (actual data range -0.1 up to 2.2).

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