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All Journal Buletin Ilmiah Sarjana Teknik Elektro
Yusuf, Ahcmad
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Electrical & Electronics Engineering

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Smart Cold Storage Based on Photovoltaic with Adaptive Fuzzy Control Approach for Guard Quality of Fish Catch on Fishing Vessels Findiastuti, Weny; Umam, Faikul; Sulaiman, Yoga Aulia; Thinakaran, Rajermani; Dafid, Ach.; Andriansyah, Adi; Yusuf, Ahcmad
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14508

Abstract

This research is motivated by the importance of maintaining the quality of fish catches on fishing vessels, which generally experience a decline in quality due to suboptimal conventional fish storage systems and limited energy supplies at sea. To address these challenges, the development of renewable energy-based cold storage technology through a Solar Power Plant (PLTS) or Photovoltaic system is needed. This research aims to design a PLTS-based smart cold storage system capable of optimally maintaining temperature stability using the Adaptive Fuzzy Control method. It is hoped that fish quality can be maintained and the economic value of fishermen's catches can be increased. This research uses an experimental approach through the design, implementation, and testing of a fuzzy logic-based adaptive control system in real-time. The performance results are then evaluated in maintaining the temperature stability of the cooling room and the efficiency of electrical energy sourced from solar panels. It is hoped that this research can provide real solutions for fishermen, support the economic independence of the fisheries sector, and support the achievement of sustainable development targets (SDGs) 7, 9 12, and 14. During the 24-hour test, the Adaptive Fuzzy Control system in a solar-based refrigerator demonstrated consistent performance in maintaining temperature stability (standard deviation σ = 3.28 °C–3.45 °C). The average refrigerator temperature was recorded at -5.44 °C with a range of -0.9 °C to -12 °C, which remains acceptable for marine fish preservation under superchilling and mild freezing conditions. The battery capacity was at an average of 89.95%, decreasing when there was no power supply and then increasing again during charging, thus reflecting adaptive energy management. The average charging speed was 3.14 A, with a peak of up to 15.6 A at 7–8 hours, then decreasing gradually as the battery was full to prevent overcharging. These findings confirm that the proposed system effectively balances cooling performance and renewable energy utilization. The use of solar photovoltaic energy directly supports SDG 7 (Affordable and Clean Energy), while system innovation and energy optimization align with SDG 9 (Industry, Innovation, and Infrastructure). The prototype demonstrates stable and efficient operation, and the design concept is scalable for practical implementation on small to medium-sized fishing vessels. A preliminary cost analysis indicates up to 50% lower operating costs compared to conventional diesel refrigeration systems.
Evaluation of the Effectiveness of Hand Gesture Recognition Using Transfer Learning on a Convolutional Neural Network Model for Integrated Service of Smart Robot Umam, Faikul; Dafid, Ach.; Sukri, Hanifudin; Asmara, Yuli Panca; Morshed, Md Monzur; Maolana, Firman; Yusuf, Ahcmad
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14507

Abstract

This study aims to develop and evaluate the effectiveness of a transfer learning model on CNN with the proposed YOLOv12 architecture for recognizing hand gestures in real time on an integrated service robot. In addition, this study compares the performance of MobileNetV3, ResNet50, and EfficientNetB0, as well as a previously funded model (YOLOv8) and the proposed YOLOv12 development model. This research contributes to SDG 4 (Quality Education), SDG 9 (Industry, Innovation and Infrastructure), and SDG 11 (Sustainable Cities and Communities) by enhancing intelligent human–robot interaction for educational and service environments. The study applies an experimental method by comparing the performance of various transfer learning models in hand gesture recognition. The custom dataset consists of annotated hand gesture images, fine-tuned to improve model robustness under different lighting conditions, camera angles, and gesture variations. Evaluation metrics include mean Average Precision (mAP), inference latency, and computational efficiency, which determine the most suitable model for deployment in integrated service robots. The test results show that the YOLOv12 model achieved an mAP@0.5 of 99.5% with an average inference speed of 1–2 ms per image, while maintaining stable detection performance under varying conditions. Compared with other CNN-based architectures (MobileNetV3, ResNet50, and EfficientNetB0), which achieved accuracies between 97% and 99%, YOLOv12 demonstrated superior performance. Furthermore, it outperformed previous research using YOLOv8 (91.6% accuracy.
Co-Authors Andriansyah, Adi Dafid, Ach Faikul Umam Maolana, Firman Morshed, Md Monzur Sukri, Hanifudin Sulaiman, Yoga Aulia Thinakaran, Rajermani WENY FINDIASTUTI, WENY Yuli Panca Asmara