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      "keywords": "Pengenalan Wajah, CNN, Transfer Learning, ResNet50, Akurasi, Face Recognition, Biometrics",
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      "abstract": "Sistem pengenalan wajah menggunakan biometrik kini memiliki peran\r\npenting dalam keamanan dan verifikasi digital, tetapi tantangan utama yang sering\r\nmuncul adalah mencari keseimbangan yang ideal antara ketepatan pengenalan dan\r\nefisiensi pemrosesan. Kesalahan dalam pemilihan struktur model dapat\r\nmenyebabkan rendahnya kehandalan sistem keamanan atau meningkatnya beban\r\npemrosesan data. Penelitian ini berfokus pada penyelesaian masalah tersebut\r\nmelalui analisis perbandingan kinerja dari tiga arsitektur model algoritma\r\nConvolutional Neural Network (CNN), yakni ResNet50, MobileNetV2, dan\r\nVGG16. Pendekatan yang digunakan dalam penelitian ini adalah Transfer Learning\r\npada dataset wajah yang bersifat publik, yang mencakup langkah-langkah seperti\r\npra-pemrosesan gambar, pelatihan model, dan evaluasi menggunakan matriks\r\nkebingungan. Hasil dari studi menunjukkan bahwa ResNet50 menghasilkan kinerja\r\nterbaik dengan akurasi mencapai 99,62% dan tingkat presisi yang sangat unggul,\r\ndiikuti oleh VGG16 yang memiliki akurasi 98,85%. Sementara itu, MobileNetV2\r\nmemperoleh akurasi 80,92% dengan desain model yang lebih ramping. Penemuan\r\nini memberikan dampak yang signifikan bagi pengembang perangkat lunak dan\r\npeneliti di bidang visi komputer dalam memilih arsitektur yang sesuai dengan\r\nkebutuhan penggunaan. Di mana ResNet50 direkomendasikan untuk sistem\r\nkeamanan yang sangat penting, sedangkan MobileNetV2 cocok digunakan di\r\nlingkungan dengan sumber daya komputasi yang terbatas.",
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      "title": "PERBANDINGAN KINERJA ALGORITMA KLASIFIKASI\r\nDALAM SISTEM PENGENALAN WAJAH",
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