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
Articles
600 Documents
<![CDATA[Pencocokan Citra Sidik Jari Menggunakan Korelasi Silang Ternormalisasi ]]>
<![CDATA[Bulkis Kanata]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 11, No 4 (2015) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v11i4.2405
<![CDATA[Fingerprint image matching is an important procedure in fingerprint recognition. Robust fingerprint image matching under a variety of different image capture conditions is difficult to achieve, because of changes in finger pressure, variation of the angle, etc. Fingerprint matching is very important for the development of fingerprint system recognition that is sensitive to finger pressure. This paper proposes a fingerprint matching algorithm that enables the so-called fingerprint template (extracted specific part (region of interest (ROI)) of a person’s fingerprints to be matched to the different fingerprint of the same person or different people taken on different time, angle and a different finger pressure using normalized cross-correlation (NCC). This algorithm was implemented in MATLAB. The results showed that the maximum NCC value for ROI of the source fingerprints and targets that was greater than 0.62 indicates a strong correlation or similarity. ]]>
<![CDATA[Sistem Peringatan Dini Untuk Pengendalian Pembatasan Jarak Fisik Dengan Metode RSSI Menggunakan Modul Wemos D1 Mini ]]>
<![CDATA[Noni Mastiana]]>;
<![CDATA[Ardian Ulvan]]>;
<![CDATA[Melvi Ulvan]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 17, No 4 (2021) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v17i4.21650
<![CDATA[Covid-19 was first reported in Indonesia in March 2020. A month later the confirmed cases had reached 1528 cases and the death toll was 136 cases. Distance restrictions, also known as physical and social distance, are one of the government's efforts to reduce the rate of increase in the number of positive Covid-19 patients. However, this policy is still not fully effective, due to the low level of community compliance. When personal awareness has not been awakened, the use of technology in the context of early warning to maintain distance is an effective solution. This article discusses the use of received signal strength indicator (RSSI) from the Wemos D1 Mini module as a model of an early warning system device to anticipate unsafe distances when someone is in a crowd. The use of RSSI in this study shows that the device made works well, where the alarm is active when the distance between devices is 1 meter. The system performance was analyzed by calculating and measuring the average RSSI error value of -34.46 dBm and the average distance error of 0.26 meters. Overall, the early warning system using this method can be used properly to estimate physical distance restrictions during a pandemic. ]]>
<![CDATA[Modifikasi Fantom ORNL_MIRD untuk Kebutuhan Simulasi Monte Carlo Pasien Radioterapi Kanker Payudara Menggunakan MCNPX ]]>
<![CDATA[Layna Miska]]>;
<![CDATA[Rini Safitri]]>;
<![CDATA[Irwandi Irwandi]]>;
<![CDATA[Elin Yusibani]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 15, No 1 (2019) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v15i1.13392
<![CDATA[In medical physics, the simulation of radiation dose distribution in the body of radiotherapy patients is generally carried out using the Monte Carlo method. But in conducting this simulation, medical physicists are often faced with the incompatibility problems of phantom geometry with simulation needs. Phantom ORNL-MIRD is the most commonly used phantom in simulation. In a radiotherapy simulation, cancer cells are usually only assumed to be in the phantom’s body without any concrete form. This can cause the administration of radiation doses that are not right on cancer cells. Phantom ORNL-MIRD is also only composed of three materials, namely bone, lung, and the rest are considered as soft tissue. This certainly provides inappropriate simulation results, especially in the case of breast cancer radiotherapy where the breast is composed of adipose tissue. This study aims to make ORNL_MIRD phantom modelling suitable for the needs of Monte Carlo simulations of breast cancer patients. The phantom modification will be carried out on the soft tissue structure of the breast and the structure of the skin tissue. Cancer cells with a diameter of 2 cm were also added to the left breast at a depth of 2.5 cm. The γ rays from radionuclide 60Co with the energy of 1.1732 MeV and 1.3325 MeV and the probabilities of 0.989 and 0.998, respectively, were exposed to the phantom. The simulation results show that there are significant differences before and after modification. Absorption dose values in the breast with a soft tissue without tumour cells were 0.31 mGy with a relative error of 0.0023, while the absorbency dose rate in the breast with soft tissue and tumour cells was 0.2 mGy with a relative error of 0.0023.]]>
<![CDATA[Sistem Otomatisasi Pembersihan Kotoran dan Pengaturan Suhu Kandang Kelinci Berbasis Arduino Mega2560 ]]>
<![CDATA[Eko Didik Widianto]]>;
<![CDATA[Mahfudhotul Khasanah]]>;
<![CDATA[Agung Budi Prasetijo]]>;
<![CDATA[Risma Septiana]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 13, No 3 (2017) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v13i3.8422
<![CDATA[The environmental factor of the cage affects the rabbit breeding. The more rabbits, the more efficient cage cleaning needs to be done. This research resulted in an automation system prototype of dirt cleaning and temperature control of rabbit cage using Arduino Mega 2560 board. The system was capable of monitoring and controlling the cage temperature of 26-36 ° C using DHT11 temperature sensor, heating, and cooling actuators. The system performed the cage manure cleaning by controlling the motion of the servo motor after processing the input from the load cell weight sensor with the capability of 5 kg. Dirt cleaning is done if the weight of the dirt is above 1000 gram. This research also applied linearization to produce temperature and weight conversion equations which yielded more accurate reading values. The temperature readings had an accuracy of ± 1 ° C, while the load cell weight reading had an accuracy of 0.05% or 2 grams.]]>
<![CDATA[Desain Sistem Pendeteksi untuk Citra Base Sub-assembly dengan Algoritma Backpropagation ]]>
<![CDATA[Kasdianto Kasdianto]]>;
<![CDATA[Siti Aisyah]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 13, No 1 (2017) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v13i1.4368
<![CDATA[Object identification technique using machine vision has been implemented in industrial of electronic manufacturers for years. This technique is commonly used for reject detection (for disqualified product based on existing standard) or defect detection. This research aims to build a reject detector of sub-assembly condition which is differed by two conditions that are missing screw and wrong position screw using neural network backpropagation. The image taken using camera will be converted into grayscale before it is processed in backpropagation methods to generate a weight value. The experiment result shows that the network architecture with two layers has the most excellent accuracy level. Using learning rate of 0.5, target error 0.015%, and the number of node 1 of 100 and node 2 of 50, the successive rate for sub-assembly detection in right condition reached 99.02% while no error occurs in detecting the wrong condition of Sub-assembly (missing screw and wrong position screw).]]>
<![CDATA[Handling Missing Value dengan Pendekatan Regresi pada Dataset Akuakultur Berukuran Kecil ]]>
<![CDATA[Ricky Afiful Maula]]>;
<![CDATA[Agus Indra Gunawan]]>;
<![CDATA[Bima Sena Bayu Dewantara]]>;
<![CDATA[M. Udin Harun Al Rasyid]]>;
<![CDATA[Setiawardhana Setiawardhana]]>;
<![CDATA[Ferry Astika Saputra]]>;
<![CDATA[Junaedi Ispianto]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 18, No 3 (2022) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v18i3.25903
<![CDATA[Shrimp cultivation is strongly influenced by pond water quality conditions. Farmers must know the appropriate action in regulating water quality that is suitable for shrimp survival. The state of water quality can be understood by measuring pond parameters using various sensors. Installing sensors equipped with artificial intelligence modules to inform water quality conditions is the right action. However, the sensor cannot be separated from errors, so it results in not being able to get data or missing data. In this case, the approach of 5 parameters of pond water quality from 13 available parameters is carried out. This paper proposes a technique to obtain lost data caused by sensor error and looks for the best model. A simple approach can be taken, such as the Handling Missing Value (HMV), which is commonly used, namely the mean, with the K-Nearest Neighbors (KNN) classifier optimized using a grid search. However, the accuracy of this technique is still low, reaching 0.739 at 20-fold cross-validation. Calculations were carried out with other methods to further improve the prediction accuracy. It was found that Linear Regression (LR) can increase accuracy up to 0.757, which outperforms different approaches such as the statistical approach to mean 0.739, mode 0.716, median 0.734, and regression approach KNN 0.742, Lasso 0.751, Passive Aggressive Regressor (PAR) 0.737, Support Vector Regression (SVR) 0.739, Kernel Ridge (KR) 0.731, and Stochastic Gradient Descent (SGD) 0.734.]]>
<![CDATA[Evaluation Of Inverse Kinematics For Quadruped Robot With Accelerometer Sensor ]]>
<![CDATA[Ahmad Iqbal Nasrudin]]>;
<![CDATA[Khairul Anam]]>;
<![CDATA[M. Agung Prawira N]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 15, No 3 (2019) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v15i3.14079
<![CDATA[Quadruped robot is one of the types of robots that move using legs 4 compiled by some of the servo motor as a driving force on each foot ft the DOF is used. However, problems arise when this robot is confronted on the inclined plane, because the burden is borne out every servo motor on the feet will be different, so can make a fast servo motor damaged. This research was conducted on the design of the quadruped robot system for stability on the inclined plane using the accelerometer sensor and the application of the inverse kinematics method with PID control of Ziegler-Nichols. The results of tests obtained response robots in stabilizing the body when faced with the inclined plane with some degree of slope of the pitch and roll. In this research was conducted some test for quadruped robot: static Testing robot against the angel of the pitch in the standby retrieved response average robot in stabilizing the body is 245 ms, static Testing robot against the angle of roll in standby retrieved response average robot in stabilizing the body is 280 ms, dynamic Testing robot against the roll and pitch in standby retrieved response average robot in stabilizing the body is 8 seconds, Static Testing robot to stabilizing the body against the angel of roll in running the largest robot oscillations obtained 10 degrees, dynamic Testing robot to stabilizing the body against the angle of roll in run retrieved response average robot in stabilizing the body is 490 ms.]]>
<![CDATA[Algoritma Enkripsi Selektif Citra Digital dalam Ranah Frekuensi Berbasis Permutasi Chaos ]]>
<![CDATA[Rinaldi Munir]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 10, No 2 (2012) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v10i2.82
<![CDATA[This paper presents a selective image encryption in frequency domain. At first, the image is transformed into frequency domain with Discrete Cosine Transform (DCT), and then DCT coefficients are scanned in zigzag, and elements of the low-frequency sub-band are extracted. Encryption is performed only on selected elements by scrambling them using 2D chaos map, namely Arnold Cat Map. Next, IDCT is applied to obtain the encrypted image. The encryption algorithm is included in lossy encryption. Experiments on both grayscale images and color images show that the images can be encrypted succesfully. Histograms of the encrypted images differ significantly from histogram of the original images, and the pixels in the encrypted images are not longer correlated.]]>
<![CDATA[Sistem Keseimbangan Robot ERISA Pada Bidang Miring Menggunakan Kontrol PD dan Sensor Fusion ]]>
<![CDATA[Novian Fajar Satria]]>;
<![CDATA[Ali Husein Alasiry]]>;
<![CDATA[Bambang Sumantri]]>;
<![CDATA[Risma Dian Alamri]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 17, No 3 (2021) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v17i3.18762
<![CDATA[The world of robotics is currently developing; many robots are created to help humans work in carrying out daily activities. Various types of robots have been created, one of which is a type of robot that resembles a human body (humanoid). Humanoid robots are developing in many countries, including Indonesia. In its development, the walking technique is a major factor in making humanoid robots. Humanoid robots have the ability to walk like humans by balancing their body positions while walking, so they don’t fall. In maintaining this balance, a tilt detection system for the robot’s position and a balancing system is needed when the robot is about to fall. So, to overcome this problem, implementation is carried out using the sensor fusion method at the output of the sensors that are used to minimize noise or interference with the sensor output value. The accuracy of the sensor output value on the position angle tilt detector can help the robot provide a balancing act. By implementing a PD control system and sensor fusion consisting of a Kalman filter and a complementary filter, the robot was successfully carried up and down the plane to a maximum slope of 12 °.]]>
<![CDATA[Pengendalian Kecepatan Gerak Quadcopter Berbasis IMU pada Gerakan Tangan dengan Metode Template Matching ]]>
<![CDATA[Nofria Hanafi]]>;
<![CDATA[Abdul Mu'iz Miftahudin]]>;
<![CDATA[Naufal Ali Akbar C]]>;
<![CDATA[R. Sanggar Dewanto]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 14, No 3 (2018) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v14i3.12032
<![CDATA[An aerial vehicle has actuator four brushless DC motors (quadcopter) which is operated using a remote control or automatically. An aerial vehicle commonly called a quadcopter is controlled by using sensor data Inertia Measurement Unit (IMU) especially gyroscope and accelerometer sensor. Motion control is a stick control with sensor data IMU as data input. The motion from the aerial vehicle is a two-dimension (2D) motion from X axis and Y axis. Data sensor IMU is read by a microcontroller and will be processed to get real data acceleration (acceleration without influenced gravitation) remotely. The distance gesture inertia and the speed at which it operates are the parameter that is converted into an air vehicle motion. Trajectory motion is changed into picture domain 10x10 pixel as the comparison with the template matching algorithm. Match motion recognition is a motion command to the quadcopter. The simple motion like vertical, horizontal, diagonal, and curve match 96% using template matching algorithm and trajectory estimation.]]>
