eprintid: 68754
rev_number: 10
eprint_status: archive
userid: 12460
dir: disk0/00/06/87/54
datestamp: 2024-11-18 01:45:50
lastmod: 2024-11-18 01:45:50
status_changed: 2024-11-18 01:45:50
type: thesis
metadata_visibility: show
contact_email: muh.khabib@uin-suka.ac.id
creators_name: Nida Muhliya Barkah, NIM.: 22206052003
title: OPTIMASI PADA TRANSFER LEARNING UNTUK KLASIFIKASI EMOSI WAJAH PADA DATASET FER-2013
ispublished: pub
subjects: 004.
divisions: S2_inf
full_text_status: restricted
keywords: Ekspresi Wajah, Pengenalan Emosi, Deep Learning, Dataset FER-2013, ResNet-50, Xception, InceptionV3, Klasifikasi
note: Pembimbing: Prof. Dr. Ir. Shofwatul Uyun, ST, M.Kom.
abstract: Facial expressions are essential in non-verbal communication, as they naturally convey human emotions during personal interactions. Emotion recognition from facial expressions through computer vision is a popular topic in affective computing. The swift advancement of deep learning has resulted in its growing application in facial emotion recognition. However, it still encounters a significant challenge due to the necessity for a substantial amount of data to operate effectively. Numerous studies have utilized transfer learning to tackle this problem, yet a standardized approach for implementing transfer learning in facial emotion recognition has not been established. This research classifies facial emotion images into seven categories using three transfer learning models: ResNet-50, Xception, and Inception V3 on the FER-2013 dataset. Various experiments, including data pre-processing, hyperparameter tuning, and model training techniques, have been conducted. The data pre-processing results show that each model requires different input image sizes to achieve the best accuracy. Hyperparameter tuning results indicate accuracy improvements of 6.3527% for ResNet-50, 4.6949% for Inception V3, and 1.039% for Xception. Augmentation experiments show that augmenting only the disgust class yields better accuracy than augmenting all classes. Model training experiments reveal that the freeze fine-tuning method is not better compared to the fine-tuning method on datasets with thousands of samples but is superior to the freeze layer method commonly used in many studies. The best accuracy values for each model are 64.8927% for ResNet-50, 65.8261% for Xception, and 66.3973% for Inception V3.
date: 2024-08-27
date_type: published
pages: 80
institution: UIN SUNAN KALIJAGA YOGYAKARTA
department: FAKULTAS SAINS DAN TEKNOLOGI
thesis_type: masters
thesis_name: other
citation:   Nida Muhliya Barkah, NIM.: 22206052003  (2024) OPTIMASI PADA TRANSFER LEARNING UNTUK KLASIFIKASI EMOSI WAJAH PADA DATASET FER-2013.  Masters thesis, UIN SUNAN KALIJAGA YOGYAKARTA.   
document_url: https://digilib.uin-suka.ac.id/id/eprint/68754/1/22206052003_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf
document_url: https://digilib.uin-suka.ac.id/id/eprint/68754/2/22206052003_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf