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All Journal Journal Serambi Engineering (JSE) Emitor: Jurnal Teknik Elektro
Firas Maulana Lasidi
Universitas Telkom
Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering

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Implementasi Sistem Gerbang Otomatis dengan Perpaduan Teknologi Pengenalan Pelat Nomor Kendaraan dan Pengenalan Wajah Ihsan Ahmad Kamal; Fransiskus Abel Pramuadi Putra; Firas Maulana Lasidi; Wahmisari Priharti; Willy Anugrah Cahyadi
Jurnal Serambi Engineering Vol. 9 No. 4 (2024): Oktober 2024
Publisher : Faculty of Engineering, Universitas Serambi Mekkah

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Abstract

Automated access control systems have been widely used in recent years due to their high accuracy and security. In this study, we present an intelligent and secure electronic gate based on facial recognition and vehicle number plate recognition. The system combines multimodal biometric technology with face recognition using the 'face_recognition' package and automatic licence plate recognition (ALPR) using YOLOv8 and PaddleOCR to enhance the security of access to restricted areas. The system was able to correctly recognise 29 out of 30 faces, while all licence plates were accurately recognised, even at different times. The results show that the automated gate system has been successfully developed and tested with an accuracy rate of 96.67%. This success demonstrates the potential use of multimodal biometric technology to improve the security and efficiency of access control systems in various applications, such as office environments, residential areas and other public facilities. Further research can be directed towards improving the resilience of the system to different environmental conditions and expanding the database of recognised faces and vehicle number plates.
Hybrid Bayesian Optimization and Deep Reinforcement Learning for Enhanced MPPT in PV Systems Under Dynamic Conditions Firas Maulana Lasidi; Jangkung Raharjo; Basuki Rahmat; Andriani Andriani
Emitor: Jurnal Teknik Elektro Vol 26, No 2: July 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/emitor.v26i2.18469

Abstract

Abstract − Conventional Maximum Power Point Tracking (MPPT) methods in photovoltaic (PV) systems frequently suffer from significant efficiency degradation when subjected to dynamic weather and Partial Shading Conditions (PSC). To address this issue, this study proposes a novel hybrid control algorithm integrating Bayesian Optimization (BO) and Deep Reinforcement Learning (DRL). The primary contribution of this research is the development of an adaptive MPPT system architecture that leverages the global exploration capabilities of BO alongside the high-precision local tuning of DRL to maximize solar energy extraction. The methodology evaluates the proposed BO-DRL agent through an ablation study within a Python simulation environment across four distinctive environmental profiles: uniform irradiance, light partial shading, heavy partial shading, and extreme dynamic conditions. In this framework, the BO component executes a probabilistic global search via Gaussian Processes to prevent the system from getting trapped in local maxima, while the DRL agent performs continuous duty cycle adjustments to minimize steady-state oscillations. Simulation results demonstrate that the hybrid approach significantly outperforms the conventional Perturb and Observe (P&O) method. Under heavy partial shading, the hybrid algorithm achieves a tracking efficiency of 96.08%, whereas the P&O method drops to 62.24% due to local peak entrapment. Under extreme dynamic scenarios, the hybrid efficiency remains robust at 93.22%, while the P&O performance drastically degrades to 39.07%. Furthermore, the ablation validation proves that standalone DRL agents fail to initialize optimally without the global search assistance from the BO unit. In conclusion, the synergistic integration of BO-DRL yields a highly robust, efficient, and adaptive MPPT control solution capable of optimizing PV energy harvesting in highly volatile environments.
Co-Authors Andriani Andriani Basuki Rahmat Fransiskus Abel Pramuadi Putra Ihsan Ahmad Kamal Jangkung Raharjo Wahmisari Priharti Willy Anugrah Cahyadi