YUDHAWARA, ADHITYO CANDRA (2025) RANCANG BANGUNG SISTEM PENDETEKSI WAJAH SEBAGAI ALAT PENDATAAN DISTRIBUSI KOMPONEN. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
The need for increased accuracy and efficiency in component data management is crucial in supporting safety and operational security within the aviation industry. Inaccurate data could result in many components and documents not being properly recorded, posing significant security risks. This research aims to develop a prototype system for the data management of components and documents in aircraft maintenance using face recognition technology. The system, designed using Raspberry Pi 4 and a Logitech C270 camera, aims to reduce human error through the automation and digitization of the data recording process. Testing results show that the system can identify faces with an accuracy of up to 95% in optimal lighting conditions and maintains an accuracy above 85% in less than ideal conditions. These results demonstrate the significant potential of face recognition technology in the aviation industry, particularly for aircraft maintenance applications. The implementation of this system is expected to support more efficient operational activities and enhance safety standards in aircraft maintenance. Keywords: aircraft component data management, face vector embedded, Face recognition, Raspberry Pi 4, Website. Kebutuhan industri penerbangan akan peningkatan akurasi dan efisiensi dalam pendataan komponen sangat krusial untuk mendukung keselamatan dan keamanan operasional. jika data tidak akurat akan banyak komponen maupun dokumen yang tidak terdata dengan baik. Penelitian ini bertujuan untuk mengembangkan prototipe sistem pendataan distribusi komponen dan dokumen pada kegiatan pemeliharaan pesawat menggunakan teknologi pengenalan wajah (face recognition). Sistem yang dirancang menggunakan Raspberry Pi 4 dan kamera Logitech C270, bertujuan untuk mengurangi human error melalui otomatisasi dan digitalisasi proses pendataan. Hasil pengujian menunjukkan bahwa sistem mampu mengidentifikasi wajah dengan tingkat akurasi hingga 95% dalam kondisi optimal dan tetap mempertahankan akurasi di atas 85% dalam kondisi kurang ideal. Hasil ini menunjukkan potensi signifikan penggunaan teknologi pengenalan wajah dalam industri penerbangan, khususnya untuk aplikasi maintenance pesawat. Implementasi sistem ini diharapkan dapat mendukung kegiatan operasional yang lebih efisien dan meningkatkan standar keselamatan dalam maintenance pesawat. Kata kunci: Face recognition, modul, Raspberry Pi 4, pendataan komponen pesawat, website.
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