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All Journal Jurnal Elkolind : Jurnal Elektronika dan Otomasi Industri Journal of Electrical Engineering and Computer (JEECOM)
Brahma Ratih Rahayu Fakhrunnia
Politeknik Negeri Malang
Control & Systems Engineering Electrical & Electronics Engineering Energy

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Performance Evaluation of Fuzzy Logic–Enhanced Perturb and Observe MPPT for Photovoltaic Systems M. Hilmi`; Muchlis Fajar Hidayat; Rijalul Haq; Brahma Ratih Rahayu Fakhrunnia
Jurnal Elektronika dan Otomasi Industri Vol. 13 No. 1 (2026): Jurnal Elkolind Vol. 13, No. 1, 2026 (Mei 2026)
Publisher : Program Studi Teknik Elektronika Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/elkolind.v13i1.9705

Abstract

Maximum Power Point Tracking (MPPT) is a crucial technique to optimize energy extraction in photovoltaic (PV) systems under varying environmental conditions such as irradiance and temperature. The conventional Perturb and Observe (P&O) algorithm is widely used due to its simplicity; however, it suffers from steady-state oscillations and slow response to environmental changes. This study proposes an improved MPPT method by integrating a fuzzy logic controller (FLC) with the conventional P&O algorithm to generate an adaptive step size for duty cycle adjustment. The system is modeled and simulated using MATLAB/Simulink, including a PV array and a boost converter, under different operating conditions such as constant irradiance, varying irradiance, and temperature variations. The performance of both methods is evaluated based on output power, voltage and current ripple, efficiency, and dynamic response. The simulation results show that the proposed FLC–P&O method reduces voltage ripple from 5.24% to 2.33% and improves settling time from 6.312 ms to 1.872 ms. Therefore, the integration of fuzzy logic with the P&O algorithm provides a simple yet effective solution to improve MPPT performance in photovoltaic systems operating under dynamic environmental conditions.
Performance Evaluation of YOLOv8-Pose for Vision-Based Fatigue Detection via Multi-Feature Behavioral Analysis Dinda Ayu Permatasari; Rifki Noviandra Lestari; Dimas Rossiawan Hendra Putra; Brahma Ratih Rahayu Fakhrunnia; Wahyu Tri Wahono; Mohammad Muallif
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 2 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i2.16982

Abstract

Driver fatigue is a significant contributor to road traffic accidents, motivating the development of non-invasive vision-based monitoring systems. This study evaluates a single-stage YOLOv8n-Pose approach for fatigue detection based on multiple behavioral indicators, including eye closure, a hand-at-mouth gesture as a yawning-related cue, and head tilt. Unlike conventional multi-stage approaches that require separate face detection and facial landmark extraction, the proposed approach jointly detects the facial region and estimates eight task-specific keypoints within a single inference process. The model was trained on 2,625 images representing variations in lighting conditions, subjects, accessories, and camera distance. Performance was evaluated using bounding-box and keypoint detection metrics, followed by live testing under different head orientations and camera distances. The model achieved a bounding-box mAP50 of 0.967 and a keypoint mAP50 of 0.817, with F1-scores of 0.90 for Eyes Open, 0.94 for Eyes Closed, and 0.97 for Hand-at-Mouth detection. Head-angle testing showed a monotonic decrease in ear-to-shoulder keypoint distance as the head tilted from the upright position, supporting its use as a head-tilt indicator within the tested conditions. Live testing identified an effective camera distance of 60–100 cm and an average processing rate of approximately 3.3–3.5 FPS. Error analysis identified backlighting as a source of incorrect eye-state predictions and hand occlusion as a cause of missed Hand-at-Mouth detections. These results demonstrate the potential of a single-stage pose-based approach for multi-indicator vision-based fatigue detection while highlighting the need for improved lighting robustness, diverse hand-pose training data, and higher inference efficiency.
Co-Authors Dimas Rossiawan Hendra Putra M. Hilmi` Mohammad Muallif Muchlis Fajar Hidayat PERMATASARI, DINDA AYU Rifki Noviandra Lestari Rijalul Haq Wahyu Tri Wahono