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All Journal Sinergi Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat
Son Ali Akbar
Department of Electrical Engineering, Faculty of Industrial Technology, Universitas Ahmad Dahlan
Arts Humanities Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Vision-guided tracking and detection using the YOLOv5 model on a logistic delivery fixed-wing UAV Son Ali Akbar; Muhammad Taufiq Dinar Akbar; Anton Yudhana; Kamarul Hawari Ghazali
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.011

Abstract

Deep learning technologies utilizing Convolutional Neural Networks (CNNs) have advanced the development of autonomous systems, particularly in the exploration of hazardous environments. This study integrates the YOLOv5 object detection model with a fixed-wing Unmanned Aerial Vehicle (UAV) to identify simulated 5x5-meter orange marker dropping kits deployed in inaccessible disaster zones. Experiments were conducted at altitudes of 45 m, 75 m, and 100 m above sea level to assess real-time detection accuracy and terrain-mapping efficiency. The system achieved a mean Average Precision (mAP) of 88% across varying altitudes, demonstrating robust performance despite environmental challenges such as false positives from similarly colored rooftops. Computational efficiency tests were performed on the Jetson Nano platform using the TensorRT engine to accelerate object detection model inference on NVIDIA GPUs. Lighting variability significantly impacted detection reliability, resulting in a reduced mAP under suboptimal illumination. To enhance precision, post-processing filters and parameter optimizations were applied, improving the balance between detection sensitivity and specificity. These findings underscore the potential of YOLOv5-enabled UAVs for rapid, high-accuracy aid localization in disaster scenarios, although adaptive threshold tuning remains critical to address environmental variability in operational settings.
Empowering melon fermers in Magelang through AI-Based smart greenhouse technology Anton Yudhana; Novi Febrianti; Son Ali Akbar; Jihad Rahmawan; Julia Mega Reski
Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Vol. 9 No. 3 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jpm.v9i3.13535

Abstract

The application of technology in the agricultural sector is a strategic step in addressing the challenges of production efficiency and adaptation to climate change. This study presents a community empowerment initiative through the implementation of an AI-based control system in a smart greenhouse to improve melon productivity in Magelang. The community service program was conducted by Ahmad Dahlan University (UAD) in collaboration with the Rukuntani Farmers Group in Salam Village, Magelang, through the introduction and implementation of the Artificial Intelligence Farming Control System (AFAS) in a smart greenhouse. The system integrates environmental sensors and leaf image cameras with Artificial Intelligence (AI) algorithms and the Internet of Things (IoT) to monitor plant conditions and automatically detect diseases. The activities were carried out in stages, including needs assessment, training, system installation, and weekly mentoring. The results showed a 25% increase in melon productivity and more consistent fruit quality. In addition, farmers’ independence in operating the technology improved, and their work patterns shifted toward data-driven precision agriculture. This program demonstrates that a participatory approach supported by appropriate technology can have a significant impact on empowering farming communities and transforming local agriculture, as well as showing the potential of participatory smart farming programs in supporting sustainable agriculture in developing regions
Co-Authors Anton Yudhana Anton Yudhana Jihad Rahmawan Julia Mega Reski Kamarul Hawari Ghazali Muhammad Taufiq Dinar Akbar Novi Febrianti