Luqman Affandi, Arwin Datumaya Wahyudi Sumari, Abdulloh, Rokhimatul Wakhidah, Inayati Machsus Izza Addin, Muhammad Auful Kirom
Visual observation to recognize and identify aerial objects is a means to protect air sovereignty, and the delay can endanger it. Visual observation can be done through Ground-to-Air (GTA) or by approaching through Air-to-Air (ATA) using binoculars. The observation needs accuracy and speed to speed up the decision-making process. We propose an Artificial Intelligence (AI) system that receives voice input to recognize and identify an Unmanned Aerial Aircraft (UAA). We employed Naïve Bayes Classifier (NBC) that processes inputs from voice-to-text tools containing the observed UAA's feature. With 70 UAA samples, the AI system achieved an accuracy of 79% and a WER of 4.16%. © 2023 The Authors. Published by Elsevier B.V.
Department of Information Technology, Politeknik Negeri Malang, Jl. Soekarno Hatta No. 9, Malang, 65141, Indonesia; Department of Industrial Technology, Adisutjipto Institute of Aerospace Engineering, Jl. Janti Blok R, Yogyakarta, 55198, Indonesia