Jurnal Rekayasa elektrika
The journal publishes original papers in the field of electrical, computer and informatics engineering which covers, but not limited to, the following scope: Electronics: Electronic Materials, Microelectronic System, Design and Implementation of Application Specific Integrated Circuits (ASIC), VLSI Design, System-on-a-Chip (SoC) and Electronic Instrumentation Using CAD Tools, digital signal & data Processing, , Biomedical Transducers and instrumentation, Medical Imaging Equipment and Techniques, Biomedical Imaging and Image Processing, Biomechanics and Rehabilitation Engineering, Biomaterials and Drug Delivery Systems; Electrical: Electrical Engineering Materials, Electric Power Generation, Transmission and Distribution, Power Electronics, Power Quality, Power Economic, FACTS, Renewable Energy, Electric Traction, Electromagnetic Compatibility, High Voltage Insulation Technologies, High Voltage Apparatuses, Lightning Detection and Protection, Power System Analysis, SCADA, Electrical Measurements; Telecommunication: Modulation and Signal Processing for Telecommunication, Information Theory and Coding, Antenna and Wave Propagation, Wireless and Mobile Communications, Radio Communication, Communication Electronics and Microwave, Radar Imaging, Distributed Platform, Communication Network and Systems, Telematics Services and Security Network; Control: Optimal, Robust and Adaptive Controls, Non Linear and Stochastic Controls, Modeling and Identification, Robotics, Image Based Control, Hybrid and Switching Control, Process Optimization and Scheduling, Control and Intelligent Systems, Artificial Intelligent and Expert System, Fuzzy Logic and Neural Network, Complex Adaptive Systems; Computer and Informatics: Computer Architecture, Parallel and Distributed Computer, Pervasive Computing, Computer Network, Embedded System, Human—Computer Interaction, Virtual/Augmented Reality, Computer Security, Software Engineering (Software: Lifecycle, Management, Engineering Process, Engineering Tools and Methods), Programming (Programming Methodology and Paradigm), Data Engineering (Data and Knowledge level Modeling, Information Management (DB) practices, Knowledge Based Management System, Knowledge Discovery in Data), Network Traffic Modeling, Performance Modeling, Dependable Computing, High Performance Computing, Computer Security, Human-Machine Interface, Stochastic Systems, Information Theory, Intelligent Systems, IT Governance, Networking Technology, Optical Communication Technology, Next Generation Media, Robotic Instrumentation, Information Search Engine, Multimedia Security, Computer Vision, Information Retrieval, Intelligent System, Distributed Computing System, Mobile Processing, Next Network Generation, Computer Network Security, Natural Language Processing, Business Process, Cognitive Systems. Signal and System: Detection, estimation and prediction for signals and systems, Pattern recognition and classification, Artificial intelligence and data analytics, Machine learning, Deep learning, Audio and speech signal processing, Image, video, and multimedia signal processing, Sensor signal processing, Biomedical signal processing and systems, Bio-inspired systems, Coding and compression, Cryptography, and information hiding
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<![CDATA[Pendeteksian Harmonisa Arus Berbasis Feed Forward Neural Network Secara Real Time ]]>
<![CDATA[Endro Wahjono]]>;
<![CDATA[Dimas Okky Anggriawan]]>;
<![CDATA[Achmad Luki Satriawan]]>;
<![CDATA[Aji Akbar Firdaus]]>;
<![CDATA[Eka Prasetyono]]>;
<![CDATA[Indhana Sudiharto]]>;
<![CDATA[Anang Tjahjono]]>;
<![CDATA[Anang Budikarso]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 16, No 1 (2020) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v16i1.15093
<![CDATA[The development of power electronics converters has been widespread in the industrial, commercial, and home applications. The device is considered to produce harmonics in non-linear loads. Harmonics cause a decrease in power quality in the electric power system. To prevent a decrease in power quality caused by harmonics in the power system, the detection of harmonics has an important role. Therefore, this paper proposed feed forward neural network (FFNN) for harmonic detection. The design of harmonic detection device is designed with a feed forward neural network method that it has two stages of information processing, namely the training stage and the testing stage. FFNN has input harmonics and THDi as output. To detect harmonics, frst training is conducted to recognize waveform patterns and calculate the fast fourier transform (FFT) process offline. Prototype using the AMC1100DUB current sensor, microcontroller and display. To validate the proposed algorithm, compared by standard measurement tool and FFT. The results show the proposed algorithm has good performance with the average percentage error compared by standard measurement tool and FFT of 5.33 %.]]>
<![CDATA[Sistem Monitoring Kolesterol Melalui Iris Mata dengan Metode Pengolahan Citra ]]>
<![CDATA[Abdul Fadlil]]>;
<![CDATA[Wahyu Sapto Aji]]>;
<![CDATA[Arief Setyo Nugroho]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 16, No 1 (2020) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v16i1.15657
<![CDATA[Early detection to determine the presence or absence of cholesterol in the body is a necessity for everyone who wants to live healthy. Many diseases can be caused by the presence of cholesterol such as heart disease, stroke, nerve disorders, kidney problems, hypertension, and etc. Therefore, cholesterol detection must be done regularly. This study discusses about the cholesterol detection system through the iris eyes using image processing and monitoring progress in continously. Detection of cholesterol can be observed with Arcus Senilis or a gray ring in the iris eyes. Tests carried using 15 samples which cholesterol identifed. The process of image processing consists of image acquisition, sharpening, segmentation, convert grayscale and binary images. Cholesterol can be identify with difference between pixel values 0 (black) and pixel values 1 (white) in binary images. Research data will be stored in an Excel format database with adding some user data. From the test, results analysis carried the try and error threshold method using values of 80, 100, 150, and getting an accuracy of 87%, 73%, and 33%. Besides, monitoring cholesterol can be carried using a system interface and database with adding the required data and can display it on excel.]]>
<![CDATA[Vol.16, No.1, April 2020 ]]>
<![CDATA[JURNAL REKAYASA ELEKTRIKA]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 16, No 1 (2020) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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<![CDATA[PenerbitJurusan Teknik Elektro dan KomputerFakultas Teknik Universitas Syiah KualaAlamat RedaksiJurusan Teknik Elektro dan KomputerFakultas Teknik Universitas Syiah KualaJl. Tgk. Syech Abdurrauf No. 7, Banda Aceh 23111Telp/Fax: 0651-7554336e-mail: jre@unsyiah.ac.idWebsite: http://jurnal.unsyiah.ac.id/JRE]]>
<![CDATA[Model Identifkasi Sinyal Jantung Pertama (S1) dan Sinyal Jantung Kedua (S2) pada Janin ]]>
<![CDATA[Ira Puspasari]]>;
<![CDATA[Jusak Jusak]]>;
<![CDATA[Weny Indah Kusumawati]]>;
<![CDATA[Ekasari Oktarina]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 16, No 1 (2020) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v16i1.14991
<![CDATA[Process of identifying fetal heart sound signals is imperative in recognizing congenital heart function that caused by many factors, such as hereditary factors and food intake of pregnant mothers. This study developed a method for processing heart signals to separate normal fetal phonocardiogram signals from noise by utilizing the Complete Empirical Mode Decomposition (CEEMD) algorithm which is integrated with the Pearson Distance metric. Heart signals that have been separated from noise are then processed using the Shannon Energy equation in order to sharpen the intensity of the first heart signal (S1) and the second heart signal (S2), but at the same time suppress the intensity of the residual noise in the signal. Based on the experiment results from 75 normal fetal heart sound cycles, the model that has been developed is able to identify the S1 signal and S2 signal, the time duration of T11 (S1-S1), and the time duration of T12 (S1-S2). Average duration of T11 and T12 acquired in this research can possibly be used as a reference for measuring the normal duration of fetal heart sound signals.]]>
<![CDATA[Autonomous Mobile Robot based on BehaviourBased Robotic using V-REP Simulator–Pioneer P3-DX Robot ]]>
<![CDATA[Esa Apriaskar]]>;
<![CDATA[Fahmizal Fahmizal]]>;
<![CDATA[Ika Cahyani]]>;
<![CDATA[Afrizal Mayub]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 16, No 1 (2020) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v16i1.15081
<![CDATA[This article describes the design and implementation of behavior-based robotic (BBR) algorithm on a wheeled mobile robot (WMR) Pioneer P3-DX in a maze exploration mission using V-REP simulator. This robot must trace and search for targets placed randomly on a labyrinth. After successfully meeting the objective, robot runs back to home position using the nearest path. Robot navigation system applies BBR algorithm to reach the target using behavior modules which work simultaneously to obtain the desired robot’s trajectory. The most fundamental behavior which is highly affordable to build on the robot system is a wall-following behavior. To make the robot could follow the wall in a safe, smooth and responsive condition, proportional-integral-derivative (PID) controller is applied. PID controller runs by utilizing the reading of sixteen proximity sensors carried on Pioneer P3-DX robot toward the expected wall distance while the robot is exploring the labyrinth. To ensure the designed system works properly, several tests were conducted, including BBR test and PID controller test. BBR test shows that the system can choose the shortest track when returning to home position. The PID controller test produces robot movement with maximum deviation and settling time for about 0.013 m and 30 seconds, respectively.]]>
<![CDATA[Penerapan Sampah Buah Tropis untuk Microbial Fuel Cell ]]>
<![CDATA[Melda Latif]]>;
<![CDATA[Arif Dwi Fajri]]>;
<![CDATA[Mumuh Muharam]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 16, No 1 (2020) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v16i1.15723
<![CDATA[Microbial Fuel Cell (MFC) is one tool that uses microbes to produce electrical energy. The main components of MFC support are anodes, cathodes, and salt bridges. In this paper, the application of tropical fruit waste to MFC is presented. Tropical fruit waste used here are pineapples, oranges, bananas, watermelons, mangos, and papayas. The fruit waste is extracted and fermented to produce microbial. The microbial produces ions attached to the anode. Measurement of system current and voltage is carried out using a digital multimeter. In pineapple substrate MFC, Voc has a maximum of 485 mV, maximum current density is 163 mA/m2 and maximum power density of 11mW/m2. The orange substrate obtained Voc maximum of 805 mV, maximum current density of 661 mA/m2 and maximumpower density of 62 mW/m2. Voc banana substrate has a maximum of 312 mV, maximum current density of 118 mA/m2 and maximum power density of 5.9 mW/m2. The Voc watermelon substrate has a maximum of 451 mV, maximum current density of 306 mA/m2 and maximum power density of 18.6 mW/m2. Voc mango substrate has a maximum of 586 mV, maximum current density of 229 mA/m2 and maximum power density of 4.3 mW/m2. Voc papaya substrate is a maximum of 338 mV, maximum current density of 58 mA/m2 and maximum power density of 2.9 mW/m2. These results show the potential for renewable electricity sources.]]>
<![CDATA[Hardware Simulation of Rear-End Collision Avoidance System Based on Fuzzy Logic ]]>
<![CDATA[Noor Cholis Basjaruddin]]>;
<![CDATA[Didin Saefudin]]>;
<![CDATA[Anggun Pancawati]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 16, No 1 (2020) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v16i1.15107
<![CDATA[Rear-end collisions are the most common type of traffc accident. On the highway, a real-end collision may involve more than two vehicles and cause a pile-up or chain-reaction crash. Referring to data released by the Australian Capital Territory (ACT), rear-end collisions which occurred throughout 2010 constituted as much as 43.65% of all collisions. In most cases, these rear-end collisions are caused by inattentive drivers, adverse road conditions and poor following distance. The Rear-end Collision Avoidance System (RCAS) is a device to help drivers to avoid rear-end collisions. The RCAS is a subsystem of Advanced Driver Assistance Systems (ADASs) and became an important part of the driverless car. This paper discusses a hardware simulation of a RCAS based on fuzzy logic using a remote control car. The Mamdani method was used as a fuzzy inference system and realized by using the Arduiono Uno microcontroller system. Simulation results showed that the fuzzy logic algorithm of RCAS can work as designed.]]>
<![CDATA[Sistem Pendaratan Otomatis pada Quadcopter menggunakan Sliding Mode Controller ]]>
<![CDATA[Zindhu Maulana Ahmad Putra]]>;
<![CDATA[Alrijadjis Alrijadjis]]>;
<![CDATA[Bambang Sumantri]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 16, No 1 (2020) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v16i1.15389
<![CDATA[A quadcopter has a very nonlinear system characteristic that is influenced by unexpected disturbances such as the influence of wind that reflected off the ground when taking off or landing. Therefore, a robust control strategy is needed to improve the quadcopter performance. In this study, the control strategy is used to resolve outdoor automatic landing problems in a stable manner using the Sliding Mode Control (SMC) algorithm. The quadcopter has six degrees of freedom (6-DoF) with only four independent inputs, this makes it impossible to control 6-DoF directly and simultaneously. To handle this, the proposed structure is a multilevel control structure, inner loop dan outer loop controller. The Inner loop controls the rotational dynamics subsystem (3-DoF), while the outer loop controls the translational dynamics subsystem (3-DoF) which is designed in conjunction with the generation of attitude angle set-point. With the concept of automatics landing can reduce the risk of accidents on a quadcopter. The SMC technique on an automatics quadcopter landing shows the results with an error in roll of ± 0.05 radians, pitch ± 0.03 radians, yaw less than 0.3 radians, and translational movements the z-axis is ± 0.2 meters.]]>
<![CDATA[Penerapan Neural Network untuk Klasifkasi Kerusakan Mutu Tomat ]]>
<![CDATA[Zilvanhisna Emka Fitri]]>;
<![CDATA[Rizkiyah Rizkiyah]]>;
<![CDATA[Abdul Madjid]]>;
<![CDATA[Arizal Mujibtamala Nanda Imron]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 16, No 1 (2020) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v16i1.15535
<![CDATA[The decrease in quality and productivity of tomatoes is caused by high rainfall, bad weather and cultivation so that the tomatoes become rotten, cracked, and spotting occurs. The government is trying to provide training to improve the quality of tomatoes for farmers. However, the training was not effective so the researchers helped create a system that was able to educate farmers in the classification of damage to tomato quality. This system serves to facilitate farmers in recognizing tomato damage thereby reducing the risk of crop failure. In this study, the classification method used is backpropagation with 7 input parameters. The input consists of morphological and texture features. The output of this classification system consists of 3 classes are blossom end rot, fruit cracking and fruit spots caused by bacterial specks. The best accuracy level of the system in classifying tomato quality damage in the training process is 89.04% and testing is 81.11%.]]>
<![CDATA[Model Identifkasi Sinyal Jantung Pertama (S1) dan Sinyal Jantung Kedua (S2) pada Janin ]]>
<![CDATA[Ira Puspasari]]>;
<![CDATA[Jusak Jusak]]>;
<![CDATA[Weny Indah Kusumawati]]>;
<![CDATA[Ekasari Oktarina]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 16, No 1 (2020) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
Show Abstract
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Download Original
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Original Source
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Check in Google Scholar
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DOI: 10.17529/jre.v16i1.14991
<![CDATA[Process of identifying fetal heart sound signals is imperative in recognizing congenital heart function that caused by many factors, such as hereditary factors and food intake of pregnant mothers. This study developed a method for processing heart signals to separate normal fetal phonocardiogram signals from noise by utilizing the Complete Empirical Mode Decomposition (CEEMD) algorithm which is integrated with the Pearson Distance metric. Heart signals that have been separated from noise are then processed using the Shannon Energy equation in order to sharpen the intensity of the first heart signal (S1) and the second heart signal (S2), but at the same time suppress the intensity of the residual noise in the signal. Based on the experiment results from 75 normal fetal heart sound cycles, the model that has been developed is able to identify the S1 signal and S2 signal, the time duration of T11 (S1-S1), and the time duration of T12 (S1-S2). Average duration of T11 and T12 acquired in this research can possibly be used as a reference for measuring the normal duration of fetal heart sound signals.]]>
