Penerapan EfficiencyNet Untuk Pembuatan Model CNN Pada Klasifikasi Bahasa Isyarat
Abstract
This study discusses the application of EfficientNet architecture in developing a Convolutional Neural Network (CNN) model for sign language classification. Sign language is a vital communication method for the deaf community, but automatic recognition remains a challenge in the field of computer vision. One of the primary issues is the limitation in accuracy and efficiency of models in recognizing complex variations of sign language in real-world conditions. EfficientNet, known for its computational efficiency, is used as a backbone to build a CNN model that can classify sign language letter patterns with high accuracy while remaining lightweight. The dataset used in this study is American Sign Language (ASL) with data augmentation techniques to enhance the variety and quality of the dataset. The dataset comprises 14,740 images of sign language letter patterns from various angles and lighting conditions. Experimental results show that the EfficientNet-based model developed achieves training and validation accuracies of 98.40% with a more efficient model size and inference time. This study demonstrates the significant potential of using EfficientNet in developing sign language classification systems that can be applied to devices with limited resources, such as mobile applications and edge computing. These findings are expected to improve accessibility and social inclusion for the deaf and speech-impaired communities. Thus, this research not only contributes to the field of pattern recognition technology but also to efforts to enhance the quality of life for individuals with communication disabilities through the development of effective and efficient assistive tools.
Downloads
References
S. Mutiara et al., “Cracteristics And Models Of Guidance Or Islamic Education For Childrend With Disabilities In The Lubuk Lintang Sub-District Community Gang Macang Besar RT 07 RW 03.” 2023, doi: https://doi.org/10.55583/jkip.v4i1.591.
I. Sari and E. Altiarika, “Sistem Pengembangan Bahasa Isyarat Untuk Berkomunikasi dengan Penyandang Disabilitas (Tunarungu),” 2023
Mariah Ulfah dan Siti Ubaidah, “Penerapan Bahasa Isyarat dalam Pembelajaran bagi Anak Berkebutuhan Khusus Tuna Rungu,” JDSR, Jambi, 2023.
S. Siallagan et al., “Peran Guru dan Orang Tua dalam Mendukung Pembelajaran Anak Tuna Rungu di Sekolah Luar Biasa (SLB) PGRI Kamal, Bangkalan”, 2024, doi: 10.62383/dilan.v1i3.475.
A. Saifudin, “PERAN KECERDASAN BUATAN DALAM MENINGKATKAN PEMBELAJARAN SISWA TUNARUNGU DI ERA DIGITAL,” 2024
I. Suhardin, A. Patombongi, A. Muhammad Islah, and S. H. Catur Sakti Kendari Jl Abdullah, “MENGIDENTIFIKASI JENIS TANAMAN BERDASARKAN CITRA DAUN MENGGUNAKAN AlGORITMA CONVOLUTIONAL NEURAL NETWORK,” vol. 6, no. 2, 2021, doi: https://doi.org/10.51876/simtek.v6i2.101.
J. Homepage, S. Sahibu, I. Taufik, and P. Studi Sistem Komputer, “Implementation of the Convolutional Neural Network Algorithm for Classifying Types of Organic and Non-Organic Waste,” vol. 4, no. 3, pp. 840–852, 2024, doi: 10.57152/malcom.v7i2.1346.
I. Maulana, N. Khairunisa, R. Mufidah Informatika, U. H. Singaperbangsa Karawang Jl Ronggo Waluyo, T. Timur, and J. Barat, “DETEKSI BENTUK WAJAH MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN),” Karawang, 2023. doi: https://doi.org/10.36040/jati.v7i6.8171.
G. Abdillah and R. Ilyas, “Deteksi Objek Bahasa Isyarat Huruf Bisindo Menggunakan SSD-Mobilenet,” 2024. doi: https://doi.org/10.30645/kesatria.v5i1.295.g292.
S. Arnandito and T. B. Sasongko, “Comparison of EfficientNetB7 and MobileNetV2 in Herbal Plant Species Classification Using Convolutional Neural Networks,” Yogyakarta, 2024. doi: https://doi.org/10.30871/jaic.v8i1.7927.
A. C. Milano, “KLASIFIKASI PENYAKIT DAUN PADI MENGGUNAKAN MODEL DEEP LEARNING EFFICIENTNET-B6,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 1, Jan. 2024, doi: 10.23960/jitet.v12i1.3855.
B. Alsharif, A. S. Altaher, A. Altaher, M. Ilyas, and E. Alalwany, “Deep Learning Technology to Recognize American Sign Language Alphabet,” Sensors, vol. 23, no. 18, Sep. 2023, doi: 10.3390/s23187970.
B. Kusuma Wardana, E. Rachmawati, T. Agung, and B. Wirayuda, “PENGENALAN GESTUR TANGAN STATIS MENGGUNAKAN CNN DENGAN ARSITEKTUR EFFICIENT-NET B4,” Bandung, 2021.
B. Y. Al-Khuraym and M. M. Ben Ismail, “Arabic Sign Language Recognition using Lightweight CNN-based Architecture,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 4, pp. 319–328, 2022, doi: 10.14569/IJACSA.2022.0130438.
T. S. Arthi and Vinotha D, “EfficientNet Indian Sign Language Recognition Using Pretrained Weights,” 2024.
F. Alrowais, S. S. Alotaibi, S. Dhahbi, R. Marzouk, A. Mohamed, and A. M. Hilal, “Sign Language Recognition and Classification Model to Enhance Quality of Disabled People,” Computers, Materials and Continua, vol. 73, no. 2, pp. 3419–3432, 2022, doi: 10.32604/cmc.2022.029438.
C. Umam Wiranda, “Pengembangan Aplikasi Mobile Android untuk Deteksi Otomatis Mata Katarak Menggunakan CNN dan Tensorflow,” Jurnal Kendali Teknik dan Sains, vol. 2, no. 3, pp. 128–140, 2024, doi: 10.59581/jkts-widyakarya.v2i3.3722.
M. Tan and Q. V Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.”, 2019
M. M. Islam, Md. A. Talukder, M. A. Uddin, A. Akhter, and M. Khalid, “BrainNet: Precision Brain Tumor Classification with Optimized EfficientNet Architecture,” International Journal of Intelligent Systems, vol. 2024, no. 1, Jan. 2024, doi: 10.1155/2024/3583612.
S. Gnanapriya and K. Rahimunnisa, “A Hybrid Deep Learning Model for Real Time Hand Gestures Recognition,” Intelligent Automation and Soft Computing, vol. 36, no. 1, pp. 1105–1119, 2023, doi: 10.32604/iasc.2023.032832.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Penerapan EfficiencyNet Untuk Pembuatan Model CNN Pada Klasifikasi Bahasa Isyarat
Pages: 758-766
Copyright (c) 2024 Amanda Kanaya Putri, Suroso Suroso, Ade Silvia Handayani
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).