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
13 Documents
Mid-Season Grand Final Prediction in Mobile Legends Professional League Indonesia Using Historical Performance Features and Comparative Machine Learning Models
Luqman Nur Fauzi
Jurnal Rekayasa Elektrika Vol. 22 No. 2 (2026): Vol. 22, No. 2, June 2026
Publisher : Universitas Syiah Kuala
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This study proposes a machine learning-based framework for mid-season Grand Final prediction in the Mobile Legends Professional League Indonesia (MPL ID) using historical regular-season performance data. Unlike conventional esports predictions that rely on subjective analysis or post-playoff evaluation, this research formulates Grand Final qualification as a binary classification problem based solely on pre-playoff statistical indicators. Team-season-level data from Seasons 10 to 16 were aggregated, with Seasons 10–15 used for training and Season 16 reserved for testing to simulate realistic future-season forecasting. Four machine learning models were evaluated: Logistic Regression, Random Forest, Support Vector Machine (SVM), and XGBoost. Although SVM and XGBoost achieved higher accuracy (88.88%), Logistic Regression demonstrated superior discriminative capability (AUC 92.85%) and the highest cross-validation stability (88.36%). Feature importance analysis identified Regular Season Rank, Clutch Factor, and Match Loss as the most influential predictors. The results indicate that structured historical aggregation combined with interpretable probabilistic modeling enables reliable estimation of Grand Final qualification before playoff brackets are formed, advancing data-driven esports analytics through an early-stage predictive framework grounded in measurable competitive indicators.
Study on the Influence of Distributed Generation on the Protection Performance of Electric Power Systems
Adi Syahputra Ritonga;
Arnawan Hasibuan;
Muhammad Daud;
Munirul Ula;
Muchlis Abdul Muthalib;
Abdullah A.Z
Jurnal Rekayasa Elektrika Vol. 22 No. 1 (2026): Vol. 22, No. 1, March 2026
Publisher : Universitas Syiah Kuala
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DOI: 10.17529/jre.v22i1.2581
Increasing integration of distributed generation (DG) based on renewable energy, such as photovoltaic (PV), is increasingly being implemented to improve the reliability of electric power distribution systems. However, DG penetration can affect the performance of the protection system, especially in managing three-phase shortcircuit fault currents. This study aims to analyze the influence of DG configuration and penetration rate on the protection system in the power distribution network, especially in the IEEE 13 Bus system. The method used is a numerical simulation in which one of the observed buses, bus 5, with the highest fault current, is selected, and a DG is inserted on buses with the lowest voltage, namely buses 2, 4, and 6. With the IEEE 13 Bus model, followed by an analysis of the fault current, voltage stability, and protection trip time for various configurations and penetration levels of DG. The results showed that the higher the DG penetration, the greater the fault current, which affected protection trip time and voltage stability. These findings suggest updating the protection scheme to account for the DG’s fault contribution and implementing stricter arrangements to optimize DG integration without compromising the reliability of the overall electric power distribution system.
Multinode Wireless Sensor on Geo-Electrical Resistivity Meter Using LoRa Communication
Sifa Nurpadillah;
Husneni Mukhtar;
Willy Anugrah Cahyadi;
Kusnahadi Susanto;
Agung Ihwan Nurdin
Jurnal Rekayasa Elektrika Vol. 22 No. 1 (2026): Vol. 22, No. 1, March 2026
Publisher : Universitas Syiah Kuala
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DOI: 10.17529/jre.v22i1.3235
Conventional geo-electrical resistivity meters require long and heavy cables to connect current and potential electrodes, making field deployment inefficient due to extensive cabling requirements. This work aims to develop and validate a Wireless Geo-Electrical Resistivity Meter (W-GERM) that reduces cable dependency by integrating a LoRa-based wireless sensor network for potential difference measurements while maintaining a wired current injection unit. The system consists of a main unit equipped with a 400 V current injector and multiple wireless multinode based on TTGO LoRa ESP32 and INA219 sensors. Performance evaluation included laboratory testing, communication and synchronization experiments, Contact Resistance Measurement (CRM) validation, and field testing using the dipole-dipole configuration at the Cikeruh River, East Bandung Basin. The proposed system provided reliable wireless communication and synchronized current injection with potential difference measurements. The calibrated INA219 sensors achieved potential measurement errors below 1%, while the CRM function reliably distinguished properly connected and improperly connected electrodes. Field validation produced subsurface resistivity profiles comparable to those obtained using the commercial AGIS Supersting R8, with a maximum survey length of 192 m and an investigation depth of approximately 20 m. The W-GERM reduces cabling complexity and provides a practical alternative for geo-electrical resistivity surveys.