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All Journal Jurnal Ilmu Komputer dan Informasi ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika
WIJAYA, JUAN THOMAS
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Computer Science & IT Electrical & Electronics Engineering Engineering Library & Information Science

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Optimasi Kinerja Fuel Cell pada Sistem Kereta Hibrida menggunakan Metode External Energy Maximization Strategy RAMADHAN, AGUNG; SUBIANTORO, ARIES; WIJAYA, JUAN THOMAS; SYAMAUN, SYAFIIE
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 11, No 2: Published April 2023
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v11i2.537

Abstract

ABSTRAKDalam sistem sumber daya hibrida, strategi manajemen energi (EMS) pada dasarnya hanya mengatur pembagian daya tanpa mempertimbangkan optimalisasi kinerja sistem. Oleh karena itu pada penelitian ini dirancang EMS berbasis optimasi pada kereta hibrida dengan sumber daya fuel cell (FC), baterai dan superkapasitor dengan metode External Energy Maximization Strategy (EEMS). Strategi ini dirancang untuk memaksimalkan energi yang disuplai oleh baterai dan superkapasitor melalui state of charge (SOC) baterai dan tegangan DC bus sehingga dapat meminimalisasi konsumsi hidrogen dan meningkatkan efisiensi keseluruhan sistem. Hasil simulasi memperlihatkan bahwa strategi ini mampu memaksimalkan kinerja baterai dan superkapasitor. Efisiensi sistem berhasil ditingkatkan menjadi 86,37% dan konsumsi hidrogen berhasil dikurangi 10% dari strategi pembandingnya. State of charge (SOC) baterai juga mampu dipertahankan untuk tetap dalam rentang batas yang telah ditentukan.Kata kunci: EMS, kereta hibrida, fuel cell, baterai, superkapasitor, optimasi ABSTRACTThe energy management strategy (EMS) in a hybrid system essentially only regulates power sharing without considering system performance optimization. This study developed an EMS based on the optimization of hybrid train with fuel cell (FC), battery, and supercapacitor power sources using the External Energy Maximization Strategy (EEMS). This strategy is intended to maximize the energy supplied by battery and supercapacitor through the SOC of the battery and DC bus voltage, thereby reducing hydrogen consumption and increasing overall system efficiency. The simulation results show that this strategy can maximize battery and supercapacitor. The system efficiency was successfully increased to 86.37%, and the hydrogen consumption was reduced by 10% when compared to the comparison strategy. The SOC of the battery can also be kept within a certain range.Keywords: EMS, hybrid train, fuel cell, battery, supercapasitor, optimization
Indonesian License Plate Detection and Recognition System using Gaussian YOLOv7 Wijaya, Juan Thomas; Arymurthy, Aniati Murni
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 2 (2025): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v18i2.1320

Abstract

In recent years, Automatic License Plate Recognition (ALPR) systems have garnered attention in computer vision research. However, practical applications face challenges such as inconsistent lighting, diverse license plate designs, and environmental variations, which increase the complexity of the task and lead to more false detections. To address these issues, we proposed Gaussian YOLOv7 for license plate detection and character recognition within ALPR systems, along with the Spatial Transformer Network (STN) for rectifying license plate orientation, aiming to enhance performance and adaptability to real-world scenarios. Additionally, we introduced a novel dataset for Indonesian ALPR systems to ensure robust detection and a balanced class distribution. Evaluation results indicate that Gaussian YOLOv7 improves precision and reduces false positives by 37.5% in the detection stage, albeit with poorer performance in other metrics. Conversely, the implementation of STN results in decreased character recognition accuracy, underscoring its limited effectiveness. Despite these challenges, Gaussian YOLOv7 excels in license plate rectification, achieving a recall of 83.8% and reducing false positives by 50.13% compared to YOLOv7. Moreover, post-processing techniques introduced by our approach further enhance precision by 5.3% and recall by 1%. Overall, our approach offers promising advancements in Indonesian ALPR systems, addressing fundamental challenges and enhancing performance.
Co-Authors Aniati Murni Arymurthy Aries Subiantoro Ramadhan, Agung SYAMAUN, SYAFIIE