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All Journal Scientific Journal of Informatics Jurnal Teknologi Dan Sistem Informasi Bisnis JATI (Jurnal Mahasiswa Teknik Informatika) Informatics, Electrical and Electronics Engineering Jurnal Hasi Penelitian Dan Pengkajian Ilmiah Eksakta - JPPIE Jurnal Sains dan Teknologi Jurnal Pemberdayaan: Publikasi Hasil Pengabdian kepada Masyarakat (JPM ITTC ) Jurnal Sistem Informasi dan Informatika Nusantara Computer and Design Review
Lisdiyanto, Angga
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Journal : Scientific Journal of Informatics

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Forensic Analysis of Drones Attacker Detection Using Deep Learning Editya, Arda Surya; Kurniati, Neny; Alamin, Mochammad Machlul; Pramana, Anggay Luri; Lisdiyanto, Angga
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.48183

Abstract

Purpose: This research proposes deep learning techniques to assist forensic analysis in drone accident cases. This process is focused on detecting attacking drones. In this research, we also compare several deep learning and make some comparisons of the best methods for detecting drone attackers.Methods: The methods applied in this research are YOLO, SSD, and Fast R-CNN. Additionally, to validate the effectiveness of the results, extensive experiments were conducted on the dataset. The dataset we use contains videos taken from drones, especially drone collisions. Evaluation metrics such as Precision, Recall, F1-Score, and mAP are used to assess the system's performance in detecting and classifying drone attackers.Results: This research show performance results in detecting and attributing drone-based threats accurately. In this experiment, it was found that YOLOV5 had superior results compared to YOLOV3 YOLOV4, SSD300, and Fast R-CNN. In this experiment we also detected ten types of objects with an average accuracy value of more than 0.5.Novelty: The proposed system contributes to improving security measures against drone-related incidents, serving as a valuable tool for law enforcement agencies, critical infrastructure protection and public safety. Furthermore, this underscores the growing importance of deep learning in addressing security challenges arising from the widespread use of drones in both civil and commercial contexts. 
Co-Authors Abdillah, Ikhwan Achmad Mufliq Agus Wibowo Andhyka, Awang Andhykad Anggay Luri Pramana Arda Surya Editya Arfi Chabibulloh, Muhammad Awang Andhyka Budiman Budiman Catur Lega Wibisono Cholilurrohman Chrisna Bayu Septrianto Djanah, Miftahul Eskaluspita, Pratama Fitrani, Laqma Dica Husaini, Arinda Putri Kurnia, Lusi Lutfiyah, Nanik Markhaban Siswanto Mochammad Machlul Alamin Mufidz Abdul Ghoffar, Muhamad Muhammad Muharrom Al Haromainy Neny Kurniati Rachman, Galih Muhamad Ridho Cahyo, Laksono Rizky Aditya Nugroho Rizqi Hisyam, Muhammad Saputro, Arief Syahri Mu’min Tri Septianto Winarti Winarti ., Winarti Winarti Winarti