Autentikasi Pilot Berbasis Pengenalan Wajah Menggunakan Convolutional Neural Network Kustom dengan Deployment TFLite

Wirpan Atmaja Putra
Heri Setiawan (Politeknik Angkatan Darat)
Yohanes Dwi Cahyono (Politeknik Angkatan Darat)

Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Vol 11 No 1 (2026): JANUARY

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.