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Hozairi
Universitas Islam Madura
Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Social Sciences Transportation Other

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Journal : Applied Information System and Management

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Classification of Sign Language in Real Time Using Convolutional Neural Network Tamam, Moh. Badri; Hozairi, Hozairi; Walid, Miftahul; Bernardo, Januario Freitas Araujo
Applied Information System and Management (AISM) Vol. 6 No. 1 (2023): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v6i1.29820

Abstract

Communication between people is essential for daily life activities. However, humans are created with their own strengths and weaknesses. One of them is the difficulty of communication and interaction for people with hearing and speech impairments. Sign language is a language for people who have difficulty hearing and speaking. However, sign language is not popular in society, and people who have it will have more difficulties. This research aims to classify hand gestures of sign language into letters using a convolutional neural network (CNN). The dataset is obtained from Kaggle, with a total of 34,627 data divided by the ratio of training and testing data of 80:20. From the test results, the letters of the alphabet that can be translated are: A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, S, T, U, V, W, X, Y, and Z. Furthermore, validation accuracy is obtained. In this study, a very high validation accuracy was obtained. The easiest letters to guess are V and N, while the most difficult letters to guess are n, c, j, and z. With different preprocessing, the loss value can be reduced, giving a higher accuracy of 95.4%.
Co-Authors AA. Masroeri Aang Kisnu Darmawan Abd. Gafur Abd. Wafi Ach. Nurul Qomar Akhmad Arif Kurdianto Alim, Syariful Alim, Syarigul Anwari Anwari Anwari, Anwari Arfianto, Afif Zuhri Artanti, M. D. Arya Yudhi Wijaya Asmara, I Putu Sindhu Bakir Bakir Bakir Bakir Bakir, Bakir Baskoro, Fajar Bernardo, Januario Freitas Araujo Billy Jhones Camerling Budhi Hascaryo Iskandar Buhari Buhari Buhari Camerling, Billy Jhones Efenie, Yuri Finanatun Halimiyah Fitra, Anis Hakiem, Luqmanul Heru Lumaksono Heru Lumaksono Heru Lumaksono Heru Lumaksono Hoiriyah, . Hoiriyah, Hoiriyah Husnul Khatimah Husnul Khatimah John Haluan Juhairiyah Juhairiyah Juhairiyah Ketut Buda Artana Kuddus, Abd Lizami, Makinul Lumaksono, Heru Lumaksono, Heru M. Ali Fikri Madukil Makruf Maisyaroh, Rohimatul Marcus Tukan Marcus Tukan Masdukil Makruf, Masdukil Maulidanitamyizi, Moh Fahmi mochamad zainul Asrori Moh. Badri Tamam Mohammad Isa Irawan Mohammad Thezar Afifudin Mohammad Thezar Afifudin Muhammad Agus Muljanto Muhsi, Muhsi Munadi Munadi Nur Azizah Nurul Badriyah Nurul Hidayat Putri, Dewi Amiliana Putri, Nadira Hijriani Rofiudin Roland Koswara Sa'diyah, Aminatus Salman Alfarisi Santosa, A. F. Syariful Alim Syariful Alim Syariful Alim Syariful Alim Taufiqurrohman Taufiqurrohman Tukan, Markus wahyudi, furqon Wahyudi, Moh. Rafiqi Walid, Miftahul Yaser Krisnafi Yaser Krisnafi, Yaser Yuri Efenie Zaifuddin, Zaifuddin