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All Journal Engineering, Mathematics and Computer Science Journal (EMACS)
Muhammad Fadlan Hidayat
Bina Nusantara University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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Deep Transfer Learning for Sign Language Image Classification: A Bisindo Dataset Study Ika Dyah Agustia Rachmawati; Rezki Yunanda; Muhammad Fadlan Hidayat; Pandu Wicaksono
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 5 No. 3 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i3.10621

Abstract

This study aims to identify and categorize the BISINDO sign language dataset, primarily consisting of image data. Deep learning techniques are used, with three pre-trained models: ResNet50 for training, MobileNetV4 for validation, and InceptionV3 for testing. The primary objective is to evaluate and compare the performance of each model based on the loss function derived during training. The training success rate provides a rough idea of the ResNet50 model's understanding of the BISINDO dataset, while MobileNetV4 measures validation loss to understand the model's generalization abilities. The InceptionV3-evaluated test loss serves as the ultimate litmus test for the model's performance, evaluating its ability to classify unobserved sign language images. The results of these exhaustive experiments will determine the most effective model and achieve the highest performance in sign language recognition using the BISINDO dataset.
Co-Authors Ika Dyah Agustia Rachmawati Pandu Wicaksono Rezki Yunanda