One-Shot Learning Face Recognition untuk Presensi Akademik Menggunakan Deep Convolutional Neural Network
Abstract
Untuk pandemi seperti sekarang ini, masyarakat lebih berhati-hati untuk kontak langsung dengan sebuah benda. Sehingga metode presensi dengan menggunakan fingerprint yang mayoritas diterapkan jadi tidak optimal. Agar tidak kontak langsung dengan mesin, pengenalan wajah dapat diterapkan sebagai pengganti proses presensi biometrik. Metode yang digunakan adalah multi-task cascaded convolutional network (MTCNN) untuk deteksi wajah dan Deep Convolutional Neural Network untuk identifikasi wajah. Perancangan aplikasi menggunakan python sebagai bahasa pemrograman, Sqlite3 sebagai basis data, Tkinter sebagai antarmuka, OpenCV & Tensorflow sebagai library pendukung, dan FaceNet & DLib sebagai framework tambahan. Aplikasi
pengenalan wajah untuk presensi dapat meningkatkan proses presensi dengan cepat dan tepat karena model yang digunakan memiliki akurasi yang hampir sempurna (Labeled Faces in the Wild 99.63% & Youtube Faces DB 95.12%) sehingga presensi yang tercatat akurat.
Kata Kunci: Presensi akademik, Deep Convolutional Neural Network, Multi-Task Convolutional Neural Network, Deteksi dan Pengenalan Wajah.
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