cover
Contact Name
Elizar
Contact Email
jre@unsyiah.ac.id
Phone
+62651-7554336
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jre@unsyiah.ac.id
Editorial Address
Jurusan Teknik Elektro dan Komputer Gedung A2 Lt. 2 Fakultas Teknik Jalan Syech Abdul Rauf no. 7 Kopelma Darussalam 23111
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Kab. aceh besar,
Aceh
INDONESIA
Jurnal Rekayasa elektrika
ISSN : 14124785     EISSN : 2252620X     DOI : https://doi.org/10.17529/hre.v19i1.15128
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 5 Documents
Search results for , issue "Vol 19, No 2 (2023)" : 5 Documents clear
Plant Monitoring Using a Web-View-Based Android Application as a Realization of the Implementation of the Smart Agriculture Concept Supriyanto Supriyanto; Rohmat Rohmat
Jurnal Rekayasa Elektrika Vol 19, No 2 (2023)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v19i2.31711

Abstract

The concept of Smart Farming has been adopted by utilizing microcontrollers, sensor devices, and actuators to regulate plant conditions. However, the communication methods with farmers, such as text messaging applications, are considered ineffective due to their limited features, and farmers cannot control monitoring devices. To address this issue, we developed a web-view-based application named Prospherine Smart Farming using PHP, Java, and MongoDB. The Software Development Life Cycle (SDLC) methodology was employed to ensure the proper functioning of the application. This application has comprehensive features that enable farmers to control monitoring devices, even remotely, and provide continuous information about farming conditions. Testing was conducted to ensure that all features functioned properly, and feedback was obtained from farmers. The research results indicate that using the Prospherine Smart Farming Application positively impacts farming activities. With this application, farmers can monitor their farming conditions in real-time and take necessary actions to enhance crop yields. The Prospherine Smart Farming Application can potentially improve agricultural efficiency and assist farmers in tackling challenges in the digital era.
Designing ANFIS Controller for MPPT on Photovoltaic System Wahyu Setyo Pambudi; Riza Agung Firmansyah; Yuliyanto Agung Prabowo; Titiek Suheta; Fathammubina Fathammubina
Jurnal Rekayasa Elektrika Vol 19, No 2 (2023)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v19i2.23394

Abstract

Photovoltaics’ current and voltage output characteristics depend on the intensity of solar radiation and temperature. Maximum Power Point works with maximum energy output and has the highest efficiency. The maximum energy point tracking method (MPPT) keeps the solar cell operating point at its maximum point. This study uses the Adaptive Neuro-Fuzzy Inference System (ANFIS) method designed and used to maintain that point. The Perturb and Observe (PnO) method is used to test the results, often used in determining this tracking. Based on the test, it was found that the average power efficiency obtained was 84.79%, and using PnO was 83.87%. The transient response using ANFIS is relatively smoother than that of using PnO, which will cause chattering when there is a change in radiation and temperature.
Gas Detection and Classification Using Neural Network Based Gas Sensors Munaf Ismail; Sri Arttini Dwi Prasetyowati
Jurnal Rekayasa Elektrika Vol 19, No 2 (2023)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v19i2.30974

Abstract

Alcoholic beverages, apart from being haram, also cause loss of consciousness. The influence of alcohol while driving is very dangerous and can result in an accident. For this reason, it is necessary to detect the alcohol content in beverages so that their halal status is known and to avoid the dangers of consuming alcohol. This research is to detect the aroma of alcohol using the MQ-3 gas sensor, which consists of an aroma sensor in general with an Artificial Neuron Network (ANN), such as the number of neurons, layers, and epoch. Most of the learning schemes require testing to optimize the model structure. For this experiment, ANN is used as a liquid classification in grouping alcoholic and non-alcoholic liquids. The MQ-3 gas sensor successfully reads liquid vapor in alcohol with levels of 30%, 50%, 70%, and other water-based liquids. An artificial neural network with 2 hidden layers, 10 neurons, and 1000 iterations with the sigmoid activation function can approach a regression score of 1.1545 and sq error score of 0.5781.
Detection of Intermittent Oscillation in Process Control Loops with Semi-Supervised Learning Nova Zidane Ibrahim; Awang Noor Indra Wardana; Agus Arif
Jurnal Rekayasa Elektrika Vol 19, No 2 (2023)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v19i2.31090

Abstract

Oscillations in the control loops indicate the poor performance of the control loops. The occurrence of oscillations in the process control loop is quite high in the industry, so it needs to be reduced so that the control loop can work properly. The first step for oscillation reduction is oscillation detection. One type of oscillation that is difficult to detect is intermittent oscillation. The smart factory concept encourages the development of the intermittent oscillation detection system using machine learning by being implemented online. Therefore, in this study an online intermittent oscillation detection program is built using K-nearest neighbor (KNN)-based Semi-supervised learning (SSL) method. The SSL method applied is self-training. The training data was obtained by a simulation of the Tennessee Eastman Process. The data is segmented based on window size and extracted time series features. The extracted data is used to build a model to detect oscillations caused by stiction, tuning errors, and external disturbances in the reactor. The model is implemented online with sliding windows and MQTT. The best accuracy and F1-score of the model obtained are 96.15% and 95.15%. In online detection, the model detects the type of oscillation with an average time of 305 seconds.
The Duration of the Cycle to Get the P Amplitude on A Discrete Electrocardiogram Sabar Setiawidayat
Jurnal Rekayasa Elektrika Vol 19, No 2 (2023)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v19i2.31605

Abstract

The P amplitude value for each cycle has not been carried out even though it is related to indications of atrial hypertrophy. The basic interpretation of the maximum P amplitude under normal conditions is 2.5 small squares on electrocardiogram (ECG) paper which is equivalent to 2.5 mV. Apart from these interpretations, an amplitude value is required that corresponds to the amount of depolarization of the atrial muscle cells. The difficulty faced by researchers is the lack of discrete ecg data available for experiments, so it only depends on amplitude data as a function of Physionet output time. An ECG is produced using discrete data but there is no electrocardiograph that displays discrete data yet. This study aims to obtain the P amplitude value based on discrete electrocardiogram data. The cycle duration value obtained from R to R is used to obtain the initial position of the cycle (sc) with the formula RN+1-1.5dR for each cycle. The P amplitude value can be obtained by filtering the maximum amplitude value between the sc and RN positions. The results of research on 10 physionet samples and 10 RSSA samples showed that all samples had an amplitude R, cycle duration and P amplitude value in each cycle.

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