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[Classification of Koilonychia, Beaus Lines, and Leukonychia based on Nail Image using Transfer Learning VGG-16 ]]>
<![CDATA[Sugondo Hadiyoso]]>;
<![CDATA[Suci Aulia]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 18, No 2 (2022) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v18i2.25694
<![CDATA[Human nail disease is usually ignored since it does not reveal clinical signs that are harmful to one's health. Nail disease, on the other hand, can be an early sign of a health issue. Some types of nail disease can cause infection, injury, or even the loss of the nail itself. It can reduce a person's aesthetics and beauty. Nail disease is very varied, so it is often difficult for clinicians to diagnose because several types have high similarities. Therefore, an automatic nail disease classification method based on nail photos was proposed in this study. The proposed method was based on the VGG-16 neural network architecture with an Adam optimizer. Nail diseases including Koilonychia, Beaus Lines, Leukonychia have been classified in this study. The model in this study is simulated in Python programming. The simulation results show that the highest classification accuracy is 96%, achieved with epoch-10. The transfer learning method based on a neural network simulated in this study is expected to support the clinical diagnosis of nail disease.]]>
<![CDATA[Deteksi Gestur Tangan Berbasis Pengolahan Citra ]]>
<![CDATA[Abdullah Sani]]>;
<![CDATA[Suci Rahmadinni]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 18, No 2 (2022) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v18i2.25147
<![CDATA[Hand sign language is a medium of communication for people with disabilities (deaf and speech impaired). However, in social practice, persons with disabilities may have to communicate with non-disable persons who do not understand sign language. These problems can be overcome with the help of translators or normal people learning sign language through existing media such as videos. Unfortunately, this method will probably cost a lot of money and time. In respons to this issue, the present study designed a sistem to detect hand gestures based on image processing. The method used is the You Only Look Once (YOLO) algorithm. The YOLO algorithm can detect and classify objects at once without being influenced by the light intensity and background of the object. This algorithm is a deep learning method that is more accurate than other deep learning methods. From this research, the system can detect and classify hand gestures with different backgrounds, light intensity, and distances with an accuracy rate above 90%.]]>
<![CDATA[Measurement of Ankle Brachial Index with Oscillometric Method for Early Detection of Peripheral Artery Disease ]]>
<![CDATA[Ervin Masita Dewi]]>;
<![CDATA[Gema Ramadhan]]>;
<![CDATA[Robinsar Parlindungan]]>;
<![CDATA[Lenny Iryani]]>;
<![CDATA[Trisno Yuwono]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 18, No 2 (2022) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v18i2.25758
<![CDATA[Peripheral Arterial Disease (PAD) is a blood vessel disease caused by blockage or plaque accumulation around the artery walls. PAD is included in the category of diseases that are often diagnosed too late and affect more severe cases, such as the death of certain tissues or body parts. The Ankle Brachial Index (ABI) is an accurate non-invasive method for diagnosing PAD, in practice, ABI is usually performed in certain hospitals and is still difficult to find due to limited tools. Therefore, a tool is made that can detect the condition of a person's PAD based on the ABI value. The tool is made using two MPX5050GP sensors to detect oscillometric pulses, a DC pump and solenoid valve as an actuator to pump and deflate the cuff, ADS1115 as an external ADC to increase the accuracy of sensor readings, as well as an LCD and buzzer as tool indicators. The output is displayed in the form of a print out from a thermal printer, with an emergency stop that functions as a safety system to power off the supply when a failure occurs in the measurement process. Oscillometric method is used to detect systolic and diastolic pressure. The accuracy of the tool is 95.5%. This accuracy result is obtained by comparing the readings of systolic and diastolic values using a sphygmomanometer which is commonly used.]]>
<![CDATA[Substraksi Latar Menggunakan Nilai Mean Untuk Klasifikasi Kendaraan Bergerak Berbasis Deep Learning ]]>
<![CDATA[Ilal Mahdi]]>;
<![CDATA[Kahlil Muchtar]]>;
<![CDATA[Fitri Arnia]]>;
<![CDATA[Tia Ernita]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 18, No 2 (2022) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v18i2.25224
<![CDATA[Moving object detection systems have been widely used in everyday life. Currently, research in the field of background subtraction is still being carried out to achieve maximum accuracy results. This study aims to model the background subtraction of an image using the mean value with the concept of non-overlapping block. Furthermore, the background abstraction results will be used in deep learning-based moving object detection. Specifically, the input image will be divided into several blocks, then the mean value of each block will be calculated to later produce a binary block (binary map). The binary blocks that have been generated will be used as input for background modeling. The background model aims to separate moving objects from the background in the input image. The resulting moving object (object localization) will be sent to the object classification stage using deep learning. The dataset used in this study is CDNet 2014. The results of the study were able to produce a more accurate moving object detection system. Quantitative tests carried out resulted in an accuracy of above 90%.]]>
<![CDATA[Perancangan Automated Guided Vehicle Menggunakan Penggerak Motor DC dan Motor Servo Berbasis Raspberry Pi 4 ]]>
<![CDATA[Florentinus Budi Setiawan]]>;
<![CDATA[Yosia Yovie Christian Wibowo]]>;
<![CDATA[Leonardus Heru Pratomo]]>;
<![CDATA[Slamet Riyadi]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 18, No 2 (2022) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v18i2.25863
<![CDATA[The influence of the industrial revolution 4.0 resulted in very significant changes. Many companies compete to produce robots that facilitate human work, in terms of energy and time in the process of producing goods. One of the robots being developed is the Automated Guided Vehicle (AGV), a vehicle with automatic control. AGV has high accuracy, easy maintenance, and a long operating time. This study discusses the design and implementation of AGV using 2 motors. The front motor using a servo motor is used for steering to turn right and turn left, while the rear motor in the form of a DC motor is used to regulate the speed of the AGV. The AGV movement system is controlled by computer vision. The AGV problem encountered is that the camera reading distance is close, which makes it less efficient in industrial use. This problem can be solved with a camera connected to a raspberry pi capable of capturing text and images from a distance of 100 cm. The use of computer vision makes the AGV robot easy to move. In this study, the accuracy of the movement of the AGV robot to the trajectory pattern has an average angle difference of 3.09°. The difference in the angle indicates a small error so that the AGV can operate optimally. Infield applications, this AGV is used in the manufacturing industry to move goods. Therefore, the use of AGV is needed because it has high accuracy and small error.]]>
<![CDATA[Rancang Bangun Driver PZT dan Filtering Data Akustik Pada Sonar Aktif ]]>
<![CDATA[Adhi Kusuma Negara]]>;
<![CDATA[Henry Manik]]>;
<![CDATA[Susilohadi Susilohadi]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 18, No 2 (2022) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v18i2.25244
<![CDATA[Acoustic transducer is a source level component in the sonar equation that is indispensable as an underwater acoustic energy source, with a certain emission pattern called beamforming. In this research, we designed the construction of a driver circuit to trigger a piezoelectric (PZT) transducer to produce an acoustic energy beam for the purpose of detecting underwater targets. The manufacture of an acoustic driver with a simple working principle has been carried out to provide transmit (TX) and receive (RX) commands. The RX module will receive the acoustic signal from the TX and the results are recorded for analysis. The mixing of the information signal with noise makes the information obtained cannot be directly verified so that a signal processing process is needed to be able to separate the information signal and noise. Calibration using a spherical target was carried out to determine the detection results and the acoustic intensity level. Signal analysis and visualization methods use Fast Fourier Transform (FFT) and wavelets.]]>
<![CDATA[Simulasi Sistem PLTS Atap dan Harga Satuan Energi Listrik Untuk Skala Rumah Tangga di Surabaya ]]>
<![CDATA[Elieser Tarigan]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 18, No 2 (2022) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v18i2.25535
<![CDATA[Solar energy is a renewable energy source that can be used as a source of electricity using a photovoltaic (PV) system to reduce our dependence on fossil energy. This paper discusses an overview of the use of a rooftop PV system in accordance with applicable regulations in Indonesia. Computer simulation was conducted to determine the potential power and output energy of the rooftop PV system in the city of Surabaya. The simulation was carried out by SolarGIS Pvplanner software. Mathematical equations are derived to estimate the unit price of electric energy for the PV system, and the calculations are done numerically. The simulation results show that the total daily energy average generated from the 3 kWP roof solar PV system in Surabaya is about 13 kWh. Meanwhile, the unit price for PV system electricity is obtained between 0.08 USD - 0.11 USD / kWh.]]>
<![CDATA[Seleksi Fitur dan Perbandingan Algoritma Klasifikasi untuk Prediksi Kelulusan Mahasiswa ]]>
<![CDATA[Junta Zeniarja]]>;
<![CDATA[Abu Salam]]>;
<![CDATA[Farda Alan Ma'ruf]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 18, No 2 (2022) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v18i2.24047
<![CDATA[Students are a major part of the life cycle of a university. The number of students graduating from a university often has a small ratio when compared to the number of students obtained in the same academic year. This small student graduation rate can be caused by several aspects, such as the many student activities accompanied by economic aspects, as well as other aspects. This makes it mandatory for a university to have a model that can take into account whether the student can graduate on time or not. One of the main factors that determine the reputation of a university is student graduation on time. The higher the level of new students at a university, with the same ratio, there must also be students who graduate on time. An increase in the number of student data and academic data occurs if many students do not graduate on time from all registered students. So that it will affect the image and reputation of the university which can later threaten the accreditation value of the university. To overcome this, we need a model that can predict student graduation so that it can be used as policy making later. The purpose of this study is to propose the best classification model by comparing the highest level of accuracy of several classification algorithms including Naïve Bayes, Random Forest, Decision Tree, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) to predict student graduation. In addition, the feature selection process is also used before the classification process to optimize the model. The use of feature selection in this model with the best features using 12 regular attribute features and 1 attribute as a label. It was found that the classification model using the Random Forest algorithm was chosen, with the highest accuracy value reaching 77.35% better than other algorithms.]]>
<![CDATA[Seleksi Fitur dan Perbandingan Algoritma Klasifikasi untuk Prediksi Kelulusan Mahasiswa ]]>
<![CDATA[Junta Zeniarja]]>;
<![CDATA[Abu Salam]]>;
<![CDATA[Farda Alan Ma'ruf]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 18, No 2 (2022) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
Show Abstract
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DOI: 10.17529/jre.v18i2.24047
<![CDATA[Students are a major part of the life cycle of a university. The number of students graduating from a university often has a small ratio when compared to the number of students obtained in the same academic year. This small student graduation rate can be caused by several aspects, such as the many student activities accompanied by economic aspects, as well as other aspects. This makes it mandatory for a university to have a model that can take into account whether the student can graduate on time or not. One of the main factors that determine the reputation of a university is student graduation on time. The higher the level of new students at a university, with the same ratio, there must also be students who graduate on time. An increase in the number of student data and academic data occurs if many students do not graduate on time from all registered students. So that it will affect the image and reputation of the university which can later threaten the accreditation value of the university. To overcome this, we need a model that can predict student graduation so that it can be used as policy making later. The purpose of this study is to propose the best classification model by comparing the highest level of accuracy of several classification algorithms including Naïve Bayes, Random Forest, Decision Tree, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) to predict student graduation. In addition, the feature selection process is also used before the classification process to optimize the model. The use of feature selection in this model with the best features using 12 regular attribute features and 1 attribute as a label. It was found that the classification model using the Random Forest algorithm was chosen, with the highest accuracy value reaching 77.35% better than other algorithms.]]>
<![CDATA[Classification of Koilonychia, Beaus Lines, and Leukonychia based on Nail Image using Transfer Learning VGG-16 ]]>
<![CDATA[Sugondo Hadiyoso]]>;
<![CDATA[Suci Aulia]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 18, No 2 (2022) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
Show Abstract
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DOI: 10.17529/jre.v18i2.25694
<![CDATA[Human nail disease is usually ignored since it does not reveal clinical signs that are harmful to one's health. Nail disease, on the other hand, can be an early sign of a health issue. Some types of nail disease can cause infection, injury, or even the loss of the nail itself. It can reduce a person's aesthetics and beauty. Nail disease is very varied, so it is often difficult for clinicians to diagnose because several types have high similarities. Therefore, an automatic nail disease classification method based on nail photos was proposed in this study. The proposed method was based on the VGG-16 neural network architecture with an Adam optimizer. Nail diseases including Koilonychia, Beaus Lines, Leukonychia have been classified in this study. The model in this study is simulated in Python programming. The simulation results show that the highest classification accuracy is 96%, achieved with epoch-10. The transfer learning method based on a neural network simulated in this study is expected to support the clinical diagnosis of nail disease.]]>
