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All Journal Jurnal Simetris JUITA : Jurnal Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Dinamisia: Jurnal Pengabdian Kepada Masyarakat Conference SENATIK STT Adisutjipto Yogyakarta Angkasa: Jurnal Ilmiah Bidang Teknologi Compiler KACANEGARA Jurnal Pengabdian pada Masyarakat Building of Informatics, Technology and Science JISKa (Jurnal Informatika Sunan Kalijaga) Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat International Journal of Engineering, Technology and Natural Sciences (IJETS) Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Innovation Information Technology and Application (JINITA) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences International Journal for Applied Information Management Formosa Journal of Multidisciplinary Research (FJMR) Indonesian Journal on Data Science Jurnal Pengabdian Widya Dharma The Indonesian Journal of Computer Science
Astika AyuningTyas, Astika
Institut Teknologi Dirgantara Adisutjipto, Yogyakarta
Aerospace Engineering Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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A Comparative Evaluation of Drone Detection Models on Aerial Imageryacross Varying Training Epochs Astika Ayuningtyas; Imam Riadi; Anton Yudhana
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 3, November 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i3.26618

Abstract

Drone detection in aerial imagery has become increasingly important in security, surveillance, and military applications. This study aims to evaluate the performance of a deep learning model in detecting drone images by varying the number of training epochs (10, 20, and 50 epochs). A drone image dataset was used to train and test the model, with performance evaluated using precision, recall, mAP@0.5, and mAP@0.5:0.95 metrics. The experimental results indicate that increasing the number of epochs significantly enhances model performance. At 10 epochs, the model achieved a precision of 0.905, recall of 0.857, mAP@0.5 of 0.904, and mAP@0.5:0.95 of 0.455. At 20 epochs, recall improved to 0.879, and mAP@0.5:0.95 increased to 0.476. The best performance was observed at 50 epochs, with a precision of 0.918, recall of 0.886, mAP@0.5 of 0.920, and mAP@0.5:0.95 of 0.494. These findings demonstrate that increasing the number of training epochs not only improves detection accuracy but also enhances the model's generalization capability. The study concludes that training for 50 epochs is the optimal configuration for achieving the best performance in drone image detection, despite requiring longer training time. These results provide practical recommendations for implementing deep learning models in real-world drone detection applications.
Artificial Intelligence-Based Aircraft Detection for Enhanced Aviation Safety and Air Traffic Management Ayuningtyas, Astika; Novelia Gunawan, Saomi; Ira Candra Dewi Wulan, Puspa; Medianto, Rully; Winiarti, Sri; Rakhmadi, Aris
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5661

Abstract

The rapid growth of international air traffic has made maintaining aviation safety and managing air traffic efficiently increasingly complex, particularly in identifying aircraft in constantly changing airspace. Traditional monitoring systems such as radar and Automatic Dependent Surveillance-Broadcast (ADS-B) have limitations in operating at low altitudes, in adverse weather, and in overcrowded environments, which can reduce the ability to understand surrounding conditions. This research proposes an artificial intelligence-based visual detection system aimed at enhancing real-time aircraft identification and improving air traffic monitoring. The system uses a YOLO-based deep learning model enhanced with a special attention mechanism and data augmentation to increase accuracy, flexibility, and operational resilience. The dataset used covers various flight situations, such as variations in light, viewing angles, and background complexity, to train the model. The model's test results show that it can correctly identify 95.24% of passenger planes, 92.4% of blimps, and 90% of fighter planes. The average overall precision (mAP) is over 90%. This system is also capable of real-time inference with precision and recall consistently above 85% under various conditions. Compared with conventional vision-based detection methods, this system demonstrates superior localization capabilities and robustness, making it suitable for use in real-world flight surveillance and air traffic management. In conclusion, this AI-based framework provides a practical and scalable solution that can improve flight safety and promote smarter air traffic management.
Co-Authors Abdul Azis Adetya Dyas Saputra Agus Basukesti Agus Basukesti, Agus Ahmad Ashari Ahmad Ashari Akbar, M. Pandu Rizky Ali Mustadi Alif Restu Pramudi Anggraini Kusumaningrum Anggraini Kusumaningrum Anggraini Kusumaningrum Anggraini Kusumaningrum, Anggraini Annurroni, Ilyas Anton Honggowibowo Anton Setiawan Honggowibowo Anton Setiawan Honggowibowo Anton Yudhana Aris Rakhmadi Arwin Datumaya Wahyudi Sumari Cessara, Deno Daseftra Dewi Retnowati, Nurcahyani Dwi Kholistyanto Dwi Nugraheny, Dwi Ellyzabeth Sukmawati Emy Setyaningsih Fakhri Yahya, Muhammad Ferryka, Putri Zudhah Gabriel Naka Sorateleng Habib Satrio Atmojo Harliyus Agustian Haruno Sajati Heni Pujiastuti Imam Riadi Ira Candra Dewi Wulan, Puspa Irawan, Ayu Endita Irawaty, Mardiana Iwan Adhicandra Kholistyanto, Dwi Lely Delvia Sipayung Leonardo Tampubolon Lopes, Jodio Blasius Lukman Nadjamuddin Machsunah, Yayuk Chayatun Mauidzhoh, Uyuunul Moh Risaldi Murinto Murinto Ningsih, Tri Widyastuti Novelia Gunawan, Saomi Nurcahyani Dewi Retnowati Nurcahyani Dewi Retnowati Nurcahyani Dewi Retnowati Nurcahyani Dewi Retnowati Nurwijayanti Kusumaningrum Nuryatno, Edi Triono Nuryatno, Edy Tri Opsidion Tegar Pratama Pamungkas, Dedi Bintang Pramudi, Alif Restu Pujiastuti, Asih Putra, Novan Ramdanu Retnowati , Nurcahyani Dewi Risaldi, Moh Rochmadi, Tri Rofiq Harun Rully Medianto, Rully Safiq Rosad Salam Aryanto Sarmini Sipayung, Lely Delvia Sri Winiarti Sucahyo, Nur SUDARYANTO SUDARYANTO Syafriza, Azizatul Alif Syam, Syahriani Tole Sutikno Uyuunul Mauidzhoh Uyuunul Mauidzoh Uyuunul Mauidzoh Wahyusari, Retno Waworuntu, Alexander Wintolo, Hero Wulandari, Rindi Nur Yenni Astuti, Yenni Yuliani Indrianingsih Yuliani Indrianingsih Yuliani Indrianingsih Yuliani Indrianingsih Yuliani Indrianingsih Yuliansah, Herman