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All Journal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Wirpan Atmaja Putra
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Autentikasi Pilot Berbasis Pengenalan Wajah Menggunakan Convolutional Neural Network Kustom dengan Deployment TFLite Wirpan Atmaja Putra; Heri Setiawan; Yohanes Dwi Cahyono
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 11 No 1 (2026): JANUARY
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v11i1.7689

Abstract

Most commercial UAV platforms still lack a mandatory pre-flight identity check, leaving ground control stations exposed to impersonation and unauthorized takeover. This paper proposes a facial recognition-based pilot authentication system using a custom four-block Convolutional Neural Network (CNN) to classify three identity classes: Pilot 1, Pilot 2, and Stranger. Trained on 2,125 facial images with data augmentation, Batch Normalization, MaxPooling, and Dropout regularization, the model achieves a test accuracy of 99.07% with a loss of 0.0613. The model is further converted into TensorFlow Lite (TFLite) format for efficient deployment on resource-constrained embedded devices, offering a computationally light alternative to conventional authentication methods as an embedded pre-flight security checkpoint for UAV ground control stations.
Co-Authors Heri setiawan Yohanes Dwi Cahyono