Sistem Pengenalan Ekspresi Wajah Menggunakan Deep Learning dengan Arsitektur MobileNetV2    : Penelitian

Tiara Adinda Puspita; Adellia Febriyanti; Ardhana Aryaldi Rahmadani; Dicky Apdillah

doi:10.31004/jerkin.v4i4.6763

Authors

Tiara Adinda Puspita
Adellia Febriyanti
Ardhana Aryaldi Rahmadani
Dicky Apdillah

DOI:

https://doi.org/10.31004/jerkin.v4i4.6763

Keywords:

Deep Learning, MobileNetV2, Facial Expression Recognition, Computer Vision, FER2013, Ekspresi Wajah.

Abstract

Perkembangan teknologi Artificial Intelligence (AI), khususnya Deep Learning, telah mendorong kemajuan sistem pengolahan citra digital (computer vision), termasuk pengenalan ekspresi wajah (Facial Expression Recognition/FER) untuk mengidentifikasi emosi manusia secara otomatis berdasarkan karakteristik wajah. Sistem ini bermanfaat di bidang pendidikan, kesehatan, keamanan, dan interaksi manusia-komputer, namun masih menghadapi tantangan seperti variasi pencahayaan, posisi wajah, dan kemiripan antar ekspresi. Penelitian ini bertujuan mengembangkan sistem pengenalan ekspresi wajah menggunakan arsitektur MobileNetV2 dengan dataset FER2013 yang berisi citra grayscale tujuh kategori ekspresi: angry, disgust, fear, happy, sad, surprise, dan neutral. Tahapan penelitian meliputi preprocessing (resize, normalisasi, dan data augmentation), pelatihan model, serta evaluasi menggunakan confusion matrix, accuracy, precision, recall, dan F1-score. Model dilatih selama 30 epoch dengan batch size 32, optimizer Adam, dan learning rate 0,001. Hasil menunjukkan MobileNetV2 mencapai accuracy 90,27%, precision 89,84%, recall 89,12%, dan F1-score 89,47%, dengan pengenalan terbaik pada ekspresi happy dan surprise. Temuan ini menunjukkan bahwa MobileNetV2 merupakan alternatif yang efektif dan efisien untuk sistem pengenalan ekspresi wajah.

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