Penerapan Convolutional Neural Network dengan Arsitektur Mobilenetv2 Pada Aplikasi Penerjemah dan Pembelajaran Bahasa Isyarat
-
Ari Hadhiwibowo
*
Sekolah Tinggi Teknologi Bandung, Bandung,
Indonesia
- Sukma Ramadhan Asri Sekolah Tinggi Teknologi Bandung, Bandung, Indonesia
- Rika Andriyanti Dinata Sekolah Tinggi Teknologi Bandung, Bandung, Indonesia (*) Corresponding Author
Abstract
Sign Language is the main communication medium for deaf people, in Indonesia itself there are several types of sign language, one of which is SIBI or Indonesian Sign Language System. This type of SIBI sign language is one type of sign language used in special school (SLB) environments. Deaf children are not yet able to use sign language and others have difficulty communicating with people around them. This research aims to help deaf children learn sign language, as well as help them communicate through sign language interpreters. The methodology applied to the application is by using a Convolutional Neural Network with the MobileNetV2 architecture. CNN is an algorithm that is included in the artificial neural network category which has the advantage of having a very high level of accuracy in classification, and MobileNetV2 is a form of Convolutional Neural Network (CNN) architecture that is able to overcome the need for excess computing resources created by Google researchers. so that it can be used on mobile devices or cell phones. The model training stage obtained an accuracy value of 98.99% with a total of 100 epochs. The model trained did not experience overfitting or underfitting so the model can be used. The testing stage uses black box testing to ensure the application is running correctly. The final stage is conducting research using TAM (Technology Acceptance Model) to measure the level of approval of the application using a Likert scale. Testing using black box testing was successful and met expectations, and the implementation of the application received an approval rate of 78.4% on average.
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References
Ahlunaza, N. (2022). Perancangan Aplikasi Sistem Informasi Geografis Lokasi Latihan Bolavoli Di Kota Jambi Berbasis Android. Jurnal Informatika Dan Rekayasa Komputer …, 1(April), 118–130.
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. In Journal of Big Data (Vol. 8, Issue 1). Springer International Publishing. https://doi.org/10.1186/s40537-021-00444-8
Assayag, Y., Oliveira, H., Souto, E., Barreto, R., & Pazzi, R. (2020). Indoor Positioning System Using Dynamic Model Estimation. Sensors (Switzerland), 20(24), 1–20. https://doi.org/10.3390/s20247003
Chandran, V., Sumithra, M. G., Karthick, A., George, T., Deivakani, M., Elakkiya, B., Subramaniam, U., & Manoharan, S. (2021). Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images. BioMed Research International, 2021. https://doi.org/10.1155/2021/5584004
Chen, P., Liu, Y., Li, W., Wang, J., Wang, J., Yang, B., & Feng, G. (2024). Semi-Supervised Learning-Enhanced Fingerprint Indoor Positioning by Exploiting an Adapted Mean Teacher Model. 1–24.
Fachruddin, Pahlevi, M. R., Ismail, M., Rasywir, E., & Pratama, Y. (2020). Analisis Usability Pada Implementasi Sistem Pengelolaan Keuangan Masjid Menggunakan USE Questionnaire. Jurnal Media Informatika Budidarma, 4, 1216–1224. https://doi.org/10.30865/mib.v4i4.2518
Informatika, J., Rekayasa, D., Jakakom, K., Prayitno, A., Syachputra, B., Arrela, I., & Prayitno, A. (2023). Perancangan Sistem Informasi Parkir Di Universitas Dinamika Bangsa Berbasis Web Jurnal Informatika Dan Rekayasa Komputer ( JAKAKOM ). 3(September), 667–674.
Lee, C. C., Koo, V. C., Lim, T. S., Lee, Y. P., & Abidin, H. (2022). A multi-layer perceptron-based approach for early detection of BSR disease in oil palm trees using hyperspectral images. Heliyon, 8(4), e09252. https://doi.org/10.1016/j.heliyon.2022.e09252
Luo, L., Wang, X., Lin, Y., Ma, X., Tan, A., Chan, R., Vardhanabhuti, V., Chu, W. C., Cheng, K.-T., & Chen, H. (2023). Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions. Electrical Engineering and Systems Science. https://doi.org/https://doi.org/10.48550/arXiv.2304.06662
Muhammad Azizi, A., Korespondensi, P., & Ariany, F. (2023). Sistem Informasi Pengajuan Cuti Pegawai Menggunakan Metode Pengujian Iso 25010 (Study Kasus : Pt Mutiara Ferindo Internusa). 4(3), 326–334.
Oliinyk, V. A., Vysotska, V., Burov, Y., Mykich, K., & Basto-Fernandes, V. (2020). Propaganda Detection in Text Data Based on NLP and machine learning. CEUR Workshop Proceedings, 2631, 132–144.
Sholihin, I., & Ariyani, F. (2023). Perancangan Sistem Informasi Pendaftaran Anggota Baru Berbasis Web Pada UKMI Ar-Rahman Universitas Teknokrat Indonesia. Jurnal Data Mining Dan Sistem Informasi, 4(1), 23. https://doi.org/10.33365/jdmsi.v4i1.2618
Srisook, N., Tuntoolavest, O., Danphitsanuparn, P., Pattana-anake, V., & Joseph, F. J. J. (2022). Convolutional Neural Network Based Nutrient Deficiency Classification in Leaves of Elaeis guineensis Jacq. International Journal of Computer Information Systems and Industrial Management Applications, 14, 19–27.
Tahirović, E., & Krivić, S. (2023). Interpretability and Explainability of Logistic Regression Model for Breast Cancer Detection. Proceedings Ofthe 15th International Conference on Agents and Artificial Intelligence (ICAART2023), 3(Icaart), 161–168. https://doi.org/10.5220/0011627600003393
Thakur, P. S., Khanna, P., Sheorey, T., & Ojha, A. (2022). Explainable vision transformer enabled convolutional neural network for plant disease identification: PlantXViT. Dl.
Toroghi, M. N., Sheikh, U. U., & Irani, S. S. (2023). Classification of COVID-19 and lung opacity using vision transformer on chest x-ray images. Journal of Physics: Conference Series, 2622(1), 012016. https://doi.org/10.1088/1742-6596/2622/1/012016
Uzun, A., Ghani, F. A., Ahmadi Najafabadi, A. M., Yenigün, H., & Tekin, İ. (2021). Indoor positioning system based on global positioning system signals with down-and up-converters in 433 MHz ISM band. Sensors, 21(13). https://doi.org/10.3390/s21134338
Wang, Y., Gao, J., Li, Z., & Zhao, L. (2020). Robust and accurate Wi-Fi fingerprint location recognition method based on deep neural network. Applied Sciences (Switzerland), 10(1). https://doi.org/10.3390/app10010321
Zheng, Q., Tian, X., Yang, M., & Wang, H. (2019). Differential learning: A powerful tool for interactive content-based image retrieval. Engineering Letters, 27(1), 202–215.
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