Recognizing Word Gesture in Sign System for Indonesian Language (SIBI) Sentences Using DeepCNN and BiLSTM

Setyono, Noer Fitria Putra; Rakun, Erdefi

doi:10.1109/icacsis47736.2019.8979772

Cited by 5 publications

(4 citation statements)

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“…The Basic CNN model was based on Keras and modified for this study. The VGG16 and MobileNetV2 models were based on high-performance image classification system architectures presented in other studies [14]- [18]. To select the CNN model with the best performance, numerous experiments were conducted with these CNN architectures.…”

Section: Proposed Cnnsmentioning

confidence: 99%

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Artificial Intelligence-Based Assessment System for Evaluating Suitable Range of Heel Height

et al. 2021

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High-heeled shoes of excessive height can severely injure shoe users. Such shoes may cause various injuries, including musculoskeletal pain, osteoarthritis, and hallux valgus. A physician can estimate an appropriate heel height limitation for an individual wearer by touching calcaneus to estimate deformation of the calcaneal varus. It would typically be impractical for a woman to seek the professional assistance of her physician when buying high-heeled shoes. A novel system was developed in this study for evaluating the maximum safe height of high-heeled shoes for female wearers. In this study, images of Achilles tendons, medial longitudinal arches, lateral longitudinal arches, and plantar pressure distributions served as the inputs in the proposed system. After the system had been trained with those images, the system could output the maximum height of high-heeled shoes for each individual wearer. In this study, two crucial methods were used for performing the evaluating system. First, Basic CNN, VGG16, and MobileNetV2 were used to evaluate images of feet. Through the experiments, the proposed artificial intelligence (AI) model achieved an accuracy of 0.88. Next, a statistics algorithm was used to modify the results obtained from the AI model. Subsequently, the error of the system declined. The mean absolute error of the proposed system which was used for evaluating the maximum height of high-heeled shoes was 1.21 cm, which is less than the typical increment for commercially available high-heeled shoes.INDEX TERMS High-heeled shoes, calcaneal varus, plantar pressure, convolutional neural network (CNN), artificial intelligence (AI)

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Section: Proposed Cnnsmentioning

confidence: 99%

“…In 2019, [17] used VGG16 for transfer learning and reached notably high accuracy (0.96) in American sign language recognition. In 2019, [18] used MobileNetV2 developed by Google for transfer learning. The accuracy reached 0.99 in sign language recognition.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Assessment System for Evaluating Suitable Range of Heel Height

et al. 2021

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“…Penelitian bahasa isyarat lain yang juga menggunakan Bahasa Isyarat Indonesia sudah dilakukan [2]. Pada penelitian [7]. DeepCNN dan BiLSTM digunakan untuk mengenali Bahasa Isyarat Indonesia.…”

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“…CNN memiliki banyak arsitektur diantaranya yaitu ResNet, AlexNet, VGG, LeNet dan MobileNet. Pada penelitian [7], ResNet digunakan sebagai ekstraksi fitur dengan menggunakan BiLSTM sebagai pengklasifikasi. Pada penelitian [11] arsitektur AlexNet dan LeNet digunakan untuk melakukan klasifikasi ASL dengan hasil yang tidak jauh berbeda yaitu AlexNet 91,618% dan LeNet 92,468%.…”

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Klasifikasi Isyarat Bahasa Indonesia Menggunakan Metode Convolutional Neural Network

Rivan

Hartoyo

2022

JuTISI

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Bahasa Isyarat Indonesia adalah isyarat kata yang awalnya diambil dari isyarat yang disampaikan oleh anak tunarungu. Bahasa isyarat adalah hal yang biasa bagi orang tuli dan bisu, tetapi tidak asing bagi orang biasa. Untuk itu diperlukan perantara alternatif yang dapat menjadi penerjemah antara penyandang tunarungu dan tuna wicara sertamasyarakat biasa. Penelitian ini bertujuan untuk mengklasifikasikan sistem isyarat bahasa Indonesia menggunakan metode Convolutional Neural Network dengan arsitektur VGG (Visual Geometric Group)-16 dan Alexnet. Data terdiri dari huruf A sampai dengan huruf Z yaitu 320 data uji, 1600 data latih, dan 320 data validasi, dan data akan diubah ukurannya menjadi ukuran 224 x 224 piksel, dilanjutkan dengan grayscale dan augmentasi. Hasil pengujian VGG-16 menunjukkan bahwa klasifikasi menggunakan VGG-16 dengan optimasi Adam mendapatkan tingkat akurasi tertinggi, yaitu 99,32% untuk setiap huruf, 91,18% untuk keseluruhan. Sedangkan hasil klasifikasi menggunakan VGG-16 dengan optimasi SGD mendapatkan tingkat akurasi terendah, yaitu 98,85% untuk setiap huruf dan 84,96% untuk keseluruhan. Sedangkan dari hasil pengujian AlexNet terlihat bahwa hasil klasifikasi menggunakan AlexNet dengan Adam optimizer mendapatkan tingkat akurasi tertinggi, yaitu 99,16% untuk setiap huruf dan 89,04% untuk keseluruhan. Sedangkan hasil klasifikasi menggunakan AlexNet dengan pengoptimal SGD mendapatkan tingkat akurasi terendah, yaitu 97,33% untuk setiap huruf dan 68,33% untuk keseluruhan.

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A Systematic Mapping of Computer Vision-Based Sign Language Recognition

Jimenez-Salas

Chacón-Rivas

2022

2022 International Conference on Inclusive Technologies and Education (CONTIE)

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Recognizing Word Gesture in Sign System for Indonesian Language (SIBI) Sentences Using DeepCNN and BiLSTM

Cited by 5 publications

References 10 publications

Artificial Intelligence-Based Assessment System for Evaluating Suitable Range of Heel Height

Artificial Intelligence-Based Assessment System for Evaluating Suitable Range of Heel Height

Klasifikasi Isyarat Bahasa Indonesia Menggunakan Metode Convolutional Neural Network

A Systematic Mapping of Computer Vision-Based Sign Language Recognition

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