Perbandingan AlexNet dan VGG untuk Pengenalan Ekspresi Wajah pada Dataset Kelas Komputasi Lanjut

Sri Nurdiati; Mohamad Khoirun Najib; Fahren Bukhari; Muhammad Reza Ardhana; Salsabilla Rahmah; Trianty Putri Blante

doi:10.33633/tc.v21i3.6373

Authors

Sri Nurdiati IPB University https://orcid.org/0000-0001-9571-7060
Mohamad Khoirun Najib IPB University
Fahren Bukhari IPB University
Muhammad Reza Ardhana IPB University
Salsabilla Rahmah IPB University
Trianty Putri Blante IPB University

DOI:

https://doi.org/10.33633/tc.v21i3.6373

Keywords:

Adam, Confusion Matrix, Pelatihan Fitur, Sensitivitas, Tingkat Positif Benar

Abstract

Pengenalan emosi memainkan peran penting dalam komunikasi yang dapat dikenali dari ekspresi wajah. Terdapat banyak metode yang dapat digunakan untuk mengenali ekspresi wajah secara automatis, seperti convolutional neural network (CNN). Penelitian ini bertujuan untuk mengimplementasikan dan membandingkan model CNN dengan arsitektur AlexNet dan VGG untuk pengenalan ekspresi wajah menggunakan bahasa pemrograman Julia. Model CNN akan digunakan untuk mengklasifikasikan tiga ekspresi yang berbeda dari tujuh orang pengekspresi. Data diproses dengan beberapa teknik augmentasi data untuk mengatasi masalah keterbatasan data. Hasil penelitian menunjukkan bahwa ketiga arsitektur dapat mengklasifikasikan ekspresi wajah dengan sangat baik, yang ditunjukkan oleh nilai rata-rata akurasi pada data training dan testing yang lebih dari 94%. Untuk memenuhi nilai cross-entropy sebesar 0.1, arsitektur VGG-11 memerlukan jumlah epoch yang paling sedikit dibandingkan arsitektur lainnya, sedangkan arsitektur AlexNet memerlukan waktu komputasi yang paling sedikit. Waktu komputasi pada proses pelatihan sebanding dengan jumlah parameter yang terkandung pada model CNN. Akan tetapi, jumlah epoch yang sedikit belum tentu membutuhkan waktu komputasi yang sedikit. Selain itu, nilai recall, presisi, dan F1-score untuk masalah klasifikasi multi-class menunjukkan hasil yang baik, yaitu lebih dari 94%.

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