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Jurnal Rekayasa elektrika
Published by Universitas Syiah Kuala
ISSN : 14124785     EISSN : 2252620X     DOI : https://doi.org/10.17529/hre.v19i1.15128
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering
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
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 5 Documents
 1 
Search results for , issue "Vol 20, No 3 (2024)" : 5 Documents clear
Nutrition Temperature and TDS Control System with Fuzzy Logic on Pak Choy Hydroponics (Brassica rapa subsp. chinensis) Sanaba, Utari; Rokhana, Rika; Setiawardhana, Setiawardhana; Wijayanto, Ardik
Jurnal Rekayasa Elektrika Vol 20, No 3 (2024)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v20i3.34322

Abstract

Hydroponics is a method of cultivation that does not use soil as a medium, allowing it to be applied in limited spaces such as urban households. One of the vegetable plants that can be grown using hydroponics is pak choy (Brassica rapa subsp. chinensis). To produce healthy pak choy plants that can efficiently absorb nutrients in a hydroponic system, several factors need to be considered, such as the level of Total Dissolved Solids (TDS) in the nutrient solution, nutrient solution temperature, and air humidity in the hydroponic environment. The ideal nutrient solution temperature for hydroponic plants falls within the range of 25-27C. In this system, a monitoring and control system will be designed to optimize the growth of pak choy plants in a Deep Flow Technique (DFT) hydroponic system. In this system, the nutrient solution temperature will be controlled with a set point of 25C using an on/off control for a peltier device. To maintain the TDS level at a set point of 1200 ppm in the nutrient solution, fuzzy logic control will be employed, generating timer-based control signals for the nutrient pump A, nutrient pump B, and water pump. The monitoring system will be displayed on an Internet of Things (IoT) dashboard platform, such as ThingSpeak.
Robust Stochastic Model Predictive Control for Autonomous Vehicle Motion Planning Subiyanto, Subiyanto; Hangga, Arimaz; Bahatmaka, Aldias; Salim, Nur Azis; Sutrisno, Deyndrawan; Yunus, Elfandy; Budi Arif Prabowo, Setya; Hilmi Farras, Muhammad; Sanggrahita, Diadora
Jurnal Rekayasa Elektrika Vol 20, No 3 (2024)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v20i3.39281

Abstract

This work presents a Robust Stochastic Model Predictive Control (RSMPC) framework for real-time motion planning autonomous vehicles, addressing the complex multi-modal vehicle interactions. The proposed framework involves adding expert policy from observations to the dataset and applying the Data Aggregation (DAgger) method to filter unsafe demonstrations and resolve expert conflicts. A Dual-Stage Attention-based Recurrent Neural Network (DA-RNN) model is integrated to predict dual class variables from the dataset, producing a set containing constraints collision-avoidance predicted to be active. The RSMPC framework enhances formulation optimization by eliminating irrelevant collision avoidance constraints, resulting in faster control signals. The framework is applied iteratively, continuously updating observations and solving the RSMPC optimization formulation in real-time. Evaluation of the DA-RNN model achieved a recall value of 0.97 and a high accuracy rate of 98.1% in predicting dual interactions, with a minimal false negative rate of 0.026, highlighting its effectiveness in capturing interaction intricacies. Validated through simulations of interactive traffic intersections, the proposed framework demonstrably excels, showing high feasibility of 99.84% and a 15-fold increase in response speed compared to the baseline. This approach ensures autonomous vehicles navigate safely and efficiently in complex traffic scenarios, paving the way for more reliable and scalable autonomous driving solutions.
Streamlining Deep Learning Network for Real-time Sea Turtle Detection Putro, Muhamad Dwisnanto; Mose, Yuliana; Andaria, Alex Copernikus; Litouw, Jane; Poekoel, Vecky Canisius; Najoan, Xaverius
Jurnal Rekayasa Elektrika Vol 20, No 3 (2024)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v20i3.35236

Abstract

Monitoring turtle behavior is a conservation effort to preserve its habitat, and the detection process is a vital initial stage. On the other hand, robotics demands a deep learning network to automatically detect the presence of sea turtles that can operate in real-time. The need for increased model speed in the inference stage has led to many lightweight vision-based detectors. This work proposes a novel turtle detection to localize multiple sea turtles using a deep learning method. A lightweight primary extractor is applied to distinguish crucial features without producing a huge computational. An excited group attention is offered as an enhancement module that can capture essential turtle components in multi-level convolutional patches. A new turtle dataset is proposed that contains lighting, blur, occlusion, and complex background challenges. The evaluation results show that the proposed model performs higher accuracy than other lightweight object detection models. High-efficiency benefits models that can be implemented on low-end devices in terms of real-time data processing speed.
The Effect of Partial Shadings on the Output Power of the Photovoltaic Modules Connected with Different Current and Voltage Characteristics Sara, Ira Devi; Aqsa SH, Aidil; Tarmizi, Tarmizi
Jurnal Rekayasa Elektrika Vol 20, No 3 (2024)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v20i3.33755

Abstract

A mismatch in the output power of photovoltaic (PV) modules in a PV array can occur due to partial shading or a module replacement. Substitution of a module in a PV array with a new one might lead to different current and voltage characteristics between the new and existing modules and result in power losses. The amount of power loss might be increased more if the PV array experiences partial shading. For this reason, this study aims to investigate the effect of partial shadings on the power output of photovoltaic arrays with different current and voltage characteristics. The PV array under test consists of 25 units of solar modules with a total cross-tied (TCT) configuration. There are five shading conditions applied to the test PV array, i.e., the short narrow (ShN), the short wide (ShW), the long narrow (LnN), the long wide (LnW), and the diagonal. A magic square method is applied to reduce the power loss when the PV modules experience partial shading conditions. The results show that the power loss due to partial shadings, either on all identical modules or partially identical, is the same. The most significant power loss occurs in the long comprehensive shading scenario, where 80% of the modules experience shading, which is 41.30%.
Improved Histogram of Oriented Gradient (HOG) Feature Extraction for Facial Expressions Classification Ramiady, Luthfiar; Arnia, Fitri; Oktiana, Maulisa; Novandri, Andri
Jurnal Rekayasa Elektrika Vol 20, No 3 (2024)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v20i3.34044

Abstract

Facial expression classification system is one of the implementations of machine learning (ML) that takes facial expression datasets, undergoes training, and then utilizes the trained results to recognize facial expressions in new facial images. The recognized facial expressions include anger, contempt, disgust, fear, happy, sadness, and surprise expressions. The method employed for facial feature extraction utilizes histogram-oriented gradient (HOG). This study proposes an enhancement method for HOG feature extraction by reducing the feature dimension into multiple sub-features based on gradient orientation intervals, referred to as HOG channel (HOG-C). Classifier testing techniques are divided into two methods for comparisonsupport vector machines (SVM) with HOG features and SVM with HOG-C features. The testing results demonstrate that SVM with HOG achieves an accuracy of 99.9% with an average training time of 18.03 minutes, while SVM with HOG-C attains a 100% accuracy with an average training time of 18.09 minutes. The testing outcomes reveal that the implementation of SVM with HOG-C successfully enhances accuracy for facial expression classification.

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