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[Optimization of Fuzzy Social Force Model Adaptive Parameter using Genetic Algorithm for Mobile Robot Navigation Control ]]>
<![CDATA[Alif Wicaksana Ramadhan]]>;
<![CDATA[Bima Sena Bayu Dewantara]]>;
<![CDATA[Setiawardhana Setiawardhana]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 19, No 1 (2023) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v19i1.28330
<![CDATA[The Social Force Model (SFM) is a popular navigation technique for mobile robots that is primarily used to simulate pedestrian movement. The SFM method's drawback is that several parameter values, such as gain, k, and impact range, σ, must be determined manually. The reaction of the SFM is frequently inappropriate for certain environmental circumstances as a result of this manual determination. In this paper, we propose employing the Fuzzy Inference System (FIS), whose rules are optimized using a Genetic Algorithm (GA) to manage the value of the gain, k, parameter adaptive. The relative distance, d, and relative angle, α, concerning the robot's obstacle are the inputs for the FIS. The test results using a 3-D realistic CoppeliaSim demonstrated that the learning outcomes of FIS rules could provide adaptive parameter values suitable for each environmental circumstance, allowing the robot to travel smoothly is represented using the robot’s heading deviation which decreasing by and reaching the goal 1.6 sec faster from the starting point to the goal, compared to the SFM with the fixed parameter value. So that the proposed method is more effective and promising when deploying on the real robot implementation.]]>
<![CDATA[Analisis Kinerja Penggabungan Logika Fuzzy dan PID pada Penjejak Matahari Dua Sumbu ]]>
<![CDATA[Muhammad Nur Hasan]]>;
<![CDATA[Yuwaldi Away]]>;
<![CDATA[Suriadi Suriadi]]>;
<![CDATA[Andri Novandri]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 19, No 1 (2023) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v19i1.15128
<![CDATA[Utilization of renewable energy from solar panel systems is increasingly being applied, but until now its utilization has not been maximized. The movement of the sun caused by rotation of the earth and cloudy condition should be taken into account to maximize the electrical energy in solar panels. In this study, a concept to calculate the movement of a two-axis sun tracker is proposed by using a combination of two controller methods, i.e. Proportional Integral Derivative (PID) and Fuzzy logic known as Fuzzy-PID (F-PID). To follow the movement of the sun, the LDR sensor is used as an input to light as well as output used to drive 2 units servo for x-axis and y-axis. Sun tracker that is used is based on tetrahedron geometry and uses three Light Dependent Resistor (LDR) sensors as input. Input and output components are connected to the Atmega 328P by using a combination of Fuzzy logic and PID programs (F-PID). Fuzzy logic programming is first performed on the Matlab application using Fuzzy Inference System (FIS), then converted into an Arduino-based programming language. The sun tracker movement and the voltage received by the solar panel will be stored into the SD card using a data logging module. Adjusting the sun tracker movement using the combined Fuzzy logic and PID method intends to maximize the electrical energy received by the solar panel. The results showed that the F-PID method obtained the maximum voltage of 5.3 V, a maximum current of 0.11 A, and a maximum power of 0.61 W.]]>
<![CDATA[Water Quality Level for Shrimp Pond at Probolinggo Area Based on Fuzzy Classification System ]]>
<![CDATA[Fithrotul Irda Amaliah]]>;
<![CDATA[Agus Indra Gunawan]]>;
<![CDATA[Taufiqurrahman Taufiqurrahman]]>;
<![CDATA[Bima Sena Bayu Dewantara]]>;
<![CDATA[Ferry Astika Saputra]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 19, No 1 (2023) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v19i1.28631
<![CDATA[Since several years ago, vaname shrimp (Litopenaeus vannamei) has been extensively cultivated in Indonesia because it has good business opportunities. In aquaculture activities, water quality is an important factor that dramatically impacts the survival and quality of shrimp in the pond. Therefore, information of water quality must be known by the farmer for obtaining a satisfactory harvest. This study aims to develop a water quality monitoring system based on information of temperature, pH, salinity, and dissolved oxygen. The data from sensors are sent to the cloud utilizing Internet of Things (IoT) technology and then classified by a fuzzy logic system. In order to help farmers easily know the water quality of their shrimp pond, four sensor data including the result of classification from fuzzy logic are sent to the phone. After a trial of the system, 100% of the data are successfully sent to the cloud (google spreadsheet). The system also successfully classified the level of water quality as the expectation of the farmer. With this system, it is hoped that it can assist farmers in monitoring the water quality of shrimp pond to improve the quality and quantity of shrimp.]]>
<![CDATA[Identification of Power Quality Disturbances Based on Fast Fourier Transform and Artificial Neural Network ]]>
<![CDATA[Dimas Okky Anggriawan]]>;
<![CDATA[Endro Wahjono]]>;
<![CDATA[Indhana Sudiharto]]>;
<![CDATA[Anang Budikarso]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 19, No 1 (2023) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v19i1.27120
<![CDATA[This paper presents the proposed algorithms for the identification of Short Duration RMS Variations and Long Duration RMS Variations combined with harmonic. The proposed algorithms are Fast Fourier Transform (FFT) and Artificial Neural Network (ANN). The Algorithms identify nine types of Power Quality (PQ) disturbances such as normal signal, voltage sag, voltage swell, under voltage, over voltage, voltage sag combined harmonic, voltage swell combined harmonic, undervoltage combined harmonic, and over voltage combined harmonic. FFT is used to obtain the frequency spectrum of each PQ disturbance with frequency sampling of 1000 Hz, data length of 200. Output FFT is used to input data for ANN. Output ANN is a type of nine PQ disturbances. The result shows that proposed algorithms (FFT combined ANN) are effective for identification, which ANN with 20 neurons in the hidden layer has an accuracy of approximately 99.95 %]]>
<![CDATA[Multi-Class Heart Abnormalities Detection Based on ECG Graph Using Transfer Learning Method ]]>
<![CDATA[Sugondo Hadiyoso]]>;
<![CDATA[Suci Aulia]]>;
<![CDATA[Indrarini Dyah Irawati]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 19, No 1 (2023) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v19i1.28637
<![CDATA[The heart is one of the vital organs in the circulatory system. Regular checkups are very important to prevent heart disease. The most basic examination is blood pressure then further examination is related to the evaluation of the electrical activity of the heart using an electrocardiogram (ECG). The ECG carries important information regarding various abnormalities of heart function. Several automated classification techniques have been proposed to facilitate diagnosis. However, not all digital ECG devices provide raw data for analysis. ECG classification method based on images can be an alternative in classification. Therefore, in this study, it is proposed to classify ECG based on signal images. The proposed classification method uses transfer learning with VGG, AlexNet, and DenseNet architectures. The method used for the classification of multi-class ECG consists of normal, PVC, Atrial Fibrilation, AFL, Bigeminy, LBBB, and APB. The simulation results generate the best accuracy of 92% and F1-score of 92%. Best performance is achieved using DenseNet architecture at 60 epochs. This study is expected to be a new reference technique in the classification of ECG signals.]]>
<![CDATA[Sistem Kontrol pada Automated Guided Vehicle Beroda Mekanum menggunakan Sliding Mode Controller ]]>
<![CDATA[Muhammad Faiz]]>;
<![CDATA[Bambang Sumantri]]>;
<![CDATA[Bima Sena Bayu Dewantara]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 19, No 1 (2023) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v19i1.28127
<![CDATA[The production in industry, are involving distribution to transport the goods. Recently, distribution activities are using unmanned vehicle, that is Automated Guided Vehicle (AGV). In real condition, AGV are facing environment with complexity of high uncertainity and unlinearity. Because of this, robust control method could be considered to be used to improve the control performance. For instance, Sliding Mode Control has good robustness to the uncertainity of the system and disturbances. However, the chattering phenomenon is one of the major issues of the sliding mode control. This phenomenon could damage the motor. This research aim to reduce chattering and improve the control performance, with modifying signum function to saturation function. This research are using ROS, V-Rep and microcontroller. Microcontroller for processing algorithm and another function. Moreover, saturation function had succcessfully reducing rise time about 30%, overshoot 16% and RMSE 0.21%.]]>
<![CDATA[Defect Detection System on Stamping Machine Using the Image Processing Method ]]>
<![CDATA[Nur Wisma Nugraha]]>;
<![CDATA[Suharayadi Pancono]]>;
<![CDATA[Gun Gun Maulana]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 19, No 1 (2023) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v19i1.29111
<![CDATA[Quality products are very influential in creating profits for the company and are also closely related to the level of customer satisfaction. The higher the quality of the products produced by a company, the higher the satisfaction felt by consumers. The biggest challenge in the production process is achieving good quality with a product defect rate close to zero defect. Defects in the product are usually small. This is of course very difficult for workers to inspect each product for a long time. Thus, manual inspection is certainly ineffective and inefficient because humans have a saturation point and get tired if they work for a long time. Previous research on detecting defective objects using image processing has been carried out but has not been able to detect up to the shape and size, while in this study it can detect up to the shape and size. Therefore, to implement an automatic product defect detection system we will use image processing and RFID technology. Image processing is processing on the image using a computer so that the image quality becomes better and produces value information for each color. Image processing techniques consist of image conversion from RGB to grayscale, thresholding (binarization), and morphological operations (segmentation). While RFID is an identification method by using a means called an RFID label or transponder to store and retrieve data remotely This study aims to implement a control system on HMI and also a detection system on defect products using a visual inspection system with the aim of getting the machine effectiveness value. One method to get this value is the Overall Equipment Effectiveness (OEE) method. It is proven by implementing a visual inspection system that gets an accuracy rate of 95.97% to detect rejected products and optimize the OEE presentation value obtained. In this study, the implementation of the production monitoring system was successfully implemented with an average OEE value of 52.49%. ]]>
<![CDATA[Model dan Kendali Modular pada Pendulum Terbalik tipe Rotary ]]>
<![CDATA[Erwin Susanto]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 19, No 1 (2023) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v19i1.28262
<![CDATA[Rotary Inverted Pendulum (RIP) is a physical system that is often used as a theoretical platform and application of non-linear, unstable, and underactuated control systems so that it poses a challenge to design controls and realize them. The mechanical construction of the system consists of a pendulum arm that rotates horizontally on the RIP base shaft and a vertical pendulum arm that swings from a downward position to an upright equilibrium position. This paper presents a model and control scheme for RIP in a modular manner, in which three controller sections are constructed and realized using Multibody Matlab. The three controller parts include: a swing-up using a positive feedback Proportional Derivative controller, a switching mode controller that works to change swing up control scheme into stabilization control when the vertical pendulum arm reaches a position around its upright equilibrium, and stabilization controller to maintain vertical arm balanced using a Proportional Derivative controller. The trajectory of the motion of the pendulum arm and the 3D visualization of the pendulum system presented using Multibody Matlab show the effectiveness of the applied method.]]>
<![CDATA[Model dan Kendali Modular pada Pendulum Terbalik tipe Rotary ]]>
<![CDATA[Erwin Susanto]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 19, No 1 (2023) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v19i1.28262
<![CDATA[Rotary Inverted Pendulum (RIP) is a physical system that is often used as a theoretical platform and application of non-linear, unstable, and underactuated control systems so that it poses a challenge to design controls and realize them. The mechanical construction of the system consists of a pendulum arm that rotates horizontally on the RIP base shaft and a vertical pendulum arm that swings from a downward position to an upright equilibrium position. This paper presents a model and control scheme for RIP in a modular manner, in which three controller sections are constructed and realized using Multibody Matlab. The three controller parts include: a swing-up using a positive feedback Proportional Derivative controller, a switching mode controller that works to change swing up control scheme into stabilization control when the vertical pendulum arm reaches a position around its upright equilibrium, and stabilization controller to maintain vertical arm balanced using a Proportional Derivative controller. The trajectory of the motion of the pendulum arm and the 3D visualization of the pendulum system presented using Multibody Matlab show the effectiveness of the applied method.]]>
<![CDATA[Optimization of Fuzzy Social Force Model Adaptive Parameter using Genetic Algorithm for Mobile Robot Navigation Control ]]>
<![CDATA[Alif Wicaksana Ramadhan]]>;
<![CDATA[Bima Sena Bayu Dewantara]]>;
<![CDATA[Setiawardhana Setiawardhana]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 19, No 1 (2023) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
Show Abstract
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Download Original
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DOI: 10.17529/jre.v19i1.28330
<![CDATA[The Social Force Model (SFM) is a popular navigation technique for mobile robots that is primarily used to simulate pedestrian movement. The SFM method's drawback is that several parameter values, such as gain, k, and impact range, σ, must be determined manually. The reaction of the SFM is frequently inappropriate for certain environmental circumstances as a result of this manual determination. In this paper, we propose employing the Fuzzy Inference System (FIS), whose rules are optimized using a Genetic Algorithm (GA) to manage the value of the gain, k, parameter adaptive. The relative distance, d, and relative angle, α, concerning the robot's obstacle are the inputs for the FIS. The test results using a 3-D realistic CoppeliaSim demonstrated that the learning outcomes of FIS rules could provide adaptive parameter values suitable for each environmental circumstance, allowing the robot to travel smoothly is represented using the robot’s heading deviation which decreasing by and reaching the goal 1.6 sec faster from the starting point to the goal, compared to the SFM with the fixed parameter value. So that the proposed method is more effective and promising when deploying on the real robot implementation.]]>
