Pengembangan Model Pengenalan Wajah Menggunakan Arsitektur MobileNetV2 untuk Presensi Murid Pengajian
DOI:
https://doi.org/10.29407/wwx38j20Abstract
Proses presensi murid pengajian yang masih dilakukan secara manual berpotensi menimbulkan kesalahan pencatatan dan membutuhkan waktu yang relatif lama. Penelitian ini bertujuan mengembangkan model pengenalan wajah berbasis MobileNetV2 untuk mendukung sistem presensi otomatis murid pengajian. Metode yang digunakan adalah deep learning dengan pendekatan transfer learning. Dataset berupa citra wajah murid pengajian melalui tahap pra pemrosesan yang meliputi pemotongan wajah, perubahan ukuran citra, normalisasi, dan augmentasi data. Model dilatih menggunakan MobileNetV2 dan dievaluasi menggunakan accuracy, precision, Recall, serta F1 Score. Hasil pengujian menunjukkan bahwa model MobileNetV2 mampu mencapai accuracy 97%, precision 97%, recall 97%, dan F1-Score 97%. Hasil tersebut menunjukkan bahwa MobileNetV2 efektif digunakan untuk pengenalan wajah dengan tingkat akurasi yang tinggi dan kompleksitas komputasi yang rendah sehingga berpotensi diterapkan pada sistem presensi otomatis di lingkungan pengajian.
Keywords:
deep learning, MobileNetV2, pengenalan wajah, presensi otomatis, transfer learning.##plugins.themes.default.displayStats.downloads##
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