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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.

Keywords

Aviation Security Convolutional Neural Network Facial Recognition Pilot Authentication Tensorflow Lite

How to Cite

Citation Format
Putra, W. A., Setiawan, H., & Cahyono, Y. D. (2027). Autentikasi Pilot Berbasis Pengenalan Wajah Menggunakan Convolutional Neural Network Kustom dengan Deployment TFLite. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 11(1), 240-248. https://doi.org/10.35870/jtik.v11i1.7689

References

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Author Biographies

Wirpan Atmaja Putra, Politeknik Angkatan Darat

Program Studi Rekayasa Keamanan Cyber, Fakultas Telekomunikasi, Politeknik Angkatan Darat, Kota Batu, Provinsi Jawa Timur, Indonesia.

Heri Setiawan, Politeknik Angkatan Darat

Program Studi Teknik Elektronika, Fakultas Elektronika, Politeknik Angkatan Darat, Kota Batu, Provinsi Jawa Timur, Indonesia.

Yohanes Dwi Cahyono, Politeknik Angkatan Darat

Program Studi Rekayasa Keamanan Cyber, Fakultas Telekomunikasi, Politeknik Angkatan Darat, Kota Batu, Provinsi Jawa Timur, Indonesia.

License

Copyright (c) 2027 Wirpan Atmaja Putra, Heri Setiawan, Yohanes Dwi Cahyono

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

This is an open-access journal distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, reproduction, and adaptation in any medium (even commercially), provided appropriate credit is given to the original author(s), a link to the license is provided, and any changes made are indicated. Authors retain copyright without restrictions and grant the journal the right of first publication as well as non-exclusive publishing rights for archiving, indexing, and scholarly dissemination.

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