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Ditemukan 10621 dokumen yang sesuai dengan query
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Language and gesture
"This landmark study examines the role of gestures in relation to speech and thought. Leading scholars, including psychologists, linguists and anthropologists, offer state-of-the-art analyses to demonstrate that gestures are not merely an embellishment of speech but are integral parts of language itself. The volume contributes to a rapidly growing field of study, offering a wide range of theoretical perspectives. It has strong cross-linguistic and cross-cultural components, examining gestures by speakers of Mayan, Australian, East Asian, as well as English and European languages. The content is diverse, including chapters on gestures during aphasia and severe stuttering, the first emergence of speech–gesture combinations in children, and a section on sign language. In a rapidly growing field of study this volume opens up the agenda for research into a new approach in understanding language, thought, and society."
Cambridge, UK: Cambridge University Press, 2000
e20394915
eBooks  Universitas Indonesia Library
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Amstrong, David F.
Gesture and the nature of language
Cambridge, UK: Cambridge University Press, 1995
417.7 ARM g
Buku Teks SO  Universitas Indonesia Library
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Gesture and sign language in human-computer interaction and embodied communication : 9th International Gesture Workshop, GW 2011, Athens, Greece, May 25-27, 2011 : revised selected papers
"This book constitutes revised selected papers from the 9th International Gesture Workshop, GW 2011, held in Athens, Greece, in May 2011. The 24 papers presented were carefully reviewed and selected from 35 submissions. They are ordered in five sections named: human computer interaction; cognitive processes; notation systems and animation; gestures and signs: linguistic analysis and tools; and gestures and speech."
Berlin: Springer-Verlag, 2012
e20407814
eBooks  Universitas Indonesia Library
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The anatomy of meaning : speech, gesture, and composite utterances / N.J. Enfield.
"How do we understand what others are trying to say? The answer cannot be found in language alone. Words are linked to hand gestures and other visible phenomena to create unified ‘composite utterances’. In this book N.J. Enfield presents original case studies of speech-with-gesture based on fieldwork carried out with speakers of Lao (a language of Southeast Asia). He examines pointing gestures (including lip and finger-pointing) and illustrative gestures (examples include depicting fish traps and tracing kinship relations). His detailed analyses focus on the semiotic unification problem, that is, how to make a single interpretation when multiple signs occur together. Enfield’s arguments have implications for all branches of science with a stake in meaning and its place in human social life. "
Cambridge, UK: Cambridge University Press, 2009
e20393663
eBooks  Universitas Indonesia Library
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Chambers, Diane P.
Communicating in sign : creative ways to learn American sign language (ASL)
New York: A Fireside Book, 1998
419 CHA c
Buku Teks  Universitas Indonesia Library
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Mallery, Garrick
Sign language among North American Indians : compared with that among other people and deaf-mutes
Paris: Mouton, 1972
419 MAL s
Buku Teks  Universitas Indonesia Library
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Sianipar, Igor Lestin author
Rekomendasi Pengembangan Desain Interaksi Aplikasi Penerjemah Sistem Isyarat Bahasa Indonesia = Design Interaction Recommendations for the Development of the Indonesian Sign Language System Application
"Sebagai bentuk interaksi sosial, komunikasi menjadi salah satu hal yang tidak dapat dihindari. Komunikasi menjadi metode yang paling mudah untuk diterapkan oleh setiap orang untuk saling bertukar informasi. Informasi yang diperoleh akan sangat bergantung pada proses komunikasi yang berlangsung. Bagi teman tuli, komunikasi menjadi hal yang cukup sulit dilakukan apabila hendak berinteraksi dengan teman dengar. Begitu juga sebaliknya, teman dengar akan kesulitan apabila melakukan hal yang serupa. Terdapat salah satu aplikasi yang dapat mengatasi kesulitan interaksi bagi teman tuli, yaitu sistem aplikasi SIBI. Sistem aplikasi SIBI mampu membantu penggunanya untuk berkomunikasi kepada sesama pengguna dengan menerjemahkan bahasa isyarat SIBI menjadi teks bahasa Indonesia begitu juga sebaliknya. Namun ternyata sistem aplikasi ini dirasa belum cukup membantu penggunanya ditinjau dari sisi desain interaksinya. Melalui permasalahan tersebut, penelitian ini hadir untuk meningkatkan kualitas desain interaksi sistem aplikasi SIBI yang diharapkan kan berdampak pada meningkatnya kualitas komunikasi bagi teman tuli. Penulis merancang suatu alternatif desain untuk menjawab permasalahan yang ada dengan menerapkan user centered design. Hasil dari desain alternatif tersebut akan ditinjau ulang hingga akhirnya menghasilkan suatu rekomendasi desain sistem aplikasi SIBI yang merupakan hasil akhir dari penelitian ini.
As a one of many forms of social interaction, communication is something that cannot be replaced. Communication is the easiest method for everyone to use to take and give the information. The information that obtained will be very useful based on the ongoing communication process before. For deaf peoples, communication becomes a difficult thing to do when they want to interact with a normal people and also a normal people will find it difficult to do the same thing. There is one application that can solve the difficulties, named SIBI application system. SIBI application system able to help the users to communicate with other users by translating the SIBI language into Indonesian text and vice versa. However, it turns out that this application system is not helpful enough to use in an interaction design point of view. Through this problem, this research is to improve the quality of the interaction design of the SIBI application system which is expected to have an impact on the quality of communication for deaf friends. The author will design an alternative design to answer the existing problems by implementing a user-centered design. The results of the alternative designs will be reviewed to finally produce a recommendation for the SIBI application system design which is the final result of this research."
Depok: Fakultas Ilmu Komputer Universitas Indonesia, 2021
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UI - Skripsi Membership  Universitas Indonesia Library
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Pardede, Maria Angel Margareth
Analisis Perbandingan Model RetinaNet dan Faster R-CNN Untuk Sistem Deteksi Bahasa Isyarat Indonesia = Comparative Analysis of Faster R-CNN and RetinaNet Models for Indonesian Sign Language Detection Systems
"Bahasa isyarat umumnya dilakukan oleh tuna rungu dan tuna wicara yang menimbulkan kesenjangan dalam berkomunikasi khususnya saat melamar pekerjaan. Ada hambatan komunikasi yang dirasakan saat proses pencarian kerja dimana pada tahun 2020 menyebutkan bahwa penyandang disabilitas yang bekerja sebanyak 7,67 juta orang (5,98% dari total pekerja di Indonesia) dibandingkan dengan jumlah pekerja dengan disabilitas di Indonesia mencapai 720.748 orang (0,53% dari total pekerja di Indonesia) pada tahun 2022 menurut BPS (Badan Pusat Statistik). Penurunan persentase dalam lapangan kerja sebagian besar disebabkan oleh praktik perekrutan yang diskriminatif oleh banyak perusahaan. Jadi, dibutuhkan sistem deteksi bahasa isyarat yang dapat mempermudah dalam penerjemahan bahasa isyarat supaya kesempatan pengguna bahasa isyarat sama dengan semua orang dalam proses pelamaran kerja dan mendapatkan pekerjaan yang layak. Skenario pengambilan data adalah dengan 2 skenario, yaitu data non augmented dan augmented. Proses training dengan dataset yang terdiri atas 348 citra training yang lalu diaugmentasi sehingga berjumlah 1.044 citra training. Hasil pengujian dengan real-time testing dilakukan dengan evaluasi model menggunakan parameter akurasi sistem (confidence score), precision, recall, dan F1 Score untuk setiap model dimana nilai confidence score model Faster R-CNN dan RetinaNet adalah 96,67% : 93,33%. Selain itu, perbandingan nilai F1 Score untuk model Faster R-CNN dan RetinaNet adalah 0,98 : 0,97, tingkat akurasi mAP Faster R-CNN dan RetinaNet yang non augmented adalah 95,3% : 90,6%, sedangkan mAP Faster R-CNN dan RetinaNet yang augmented adalah 92,1% : 88,2%. Melalui hasil tersebut diperoleh bahwa kedua model memiliki presisi yang lebih rendah saat sudah diaugmentasi. Maka dari itu, algoritma Faster R-CNN memiliki hasil presisi lebih akurat dibandingkan algoritma RetinaNet.
Sign language is generally used by the deaf and speech impaired which causes errors in communication, especially when applying for jobs. There are communication barriers that are felt during the job search process where in 2020 it is stated that 7,67 million people with disabilities work (5,98% of total workers in Indonesia) compared to the number of workers with disabilities in Indonesia reaching 720,748 people (0,53% of total workers in Indonesia) in 2022 according to BPS (Badan Pusat Statistik). The percentage decline in employment is largely due to discriminatory hiring practices by many companies. So, a sign language detection system is needed that can make it easier to translate sign language so that sign language users have the same opportunities as everyone else in the job application process and getting a decent job. The data collection scenario is with 2 scenarios, namely non-augmented and augmented data. The training process uses a dataset consisting of 348 training images which are then augmented so that the total is 1.044 training images. Test results using real-time testing were carried out by evaluating the model using system accuracy parameters (confidence score), precision, recall, and F1 Score for each model where the Confidence Score value for the Faster R-CNN and RetinaNet models was 96,67% : 93,33%. In addition, the comparison of the F1 Score values​​for the Faster R-CNN and RetinaNet models is 0,98 : 0,97, the accuracy level of the non-augmented mAP Faster R-CNN and RetinaNet is 95,3% : 90,6%, while the mAP Faster R-CNN and augmented RetinaNet are 92,1% : 88,2%. From these results, it was found that the two models had lower precision when they were augmented. Therefore, the Faster R-CNN algorithm has more accurate precision results than the RetinaNet algorithm."
Depok: Fakultas Teknik Universitas Indonesia, 2024
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UI - Skripsi Membership  Universitas Indonesia Library
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I Gusti Bagus Hadi Widhinugraha
Pengenalan Gesture dan Non Gesture pada kalimat SIBI (Sistem Isyarat Bahasa Indonesia) menggunakan TCRF (Threshold Conditional Random Field) = Word-gesture and Transitional-gesture Recognition of Indonesian Sign System (known as SIBI) Sentence using Threshold Conditional Random Field
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Bahasa isyarat merupakan suatu tatanan gerakan yang mewakili suatu kosakata pada bahasa tertentu dan memiliki fungsi untuk membantu penyandang tunarungu dalam mengatasi masalah berkomunikasi. Namun tidak semua masyarakat umum menguasai bahasa isyarat. Dari permasalahan tersebut, sistem penerjemah bahasa isyarat diperlukan dalam membantu proses komunikasi penyandang tunarungu. Sistem penerjemah memerlukan sebuah video gerakan bahasa isyarat untuk kemudian dapat dikenali Dalam sebuah video utuh yang berisi satu sequence gerakan kalimat isyarat terdapat dua jenis gerakan yaitu gerakan isyarat (gesture) yang mengandung arti dan gerakan transisi (non gesture). Pada penelitian ini diusulkan metode untuk menngenali gesture dan non gesture pada kalimat SIBI (Sistem Isyarat Bahasa Indonesia) menggunakan Threshold Conditional Random Field (TCRF). Data yang digunakan adalah 2.255 video rekaman gerakan untuk 28 isyarat kalimat pada SIBI yang di peragakan oleh  tiga orang guru dan dua orang murid dari SLB Santi Rama Jakarta. Untuk merepresentasikan data, pada penelitian ini dibandingkan teknik ekstraksi fitur skeleton, image, gabungan (gabungan antara fitur skeleton dan fitur image) dan MobileNetV2. Untuk klasifikasi digunakan metode TCRF dengan variasi nilai threshold dari 1 sampai 4. Berdasarkan hasil eksperimen, masing-masing teknik ekstraksi fitur menghasilkan akurasi terbaik sebesar 72.5% untuk skeleton dengan threshold 2, 70.3% untuk image dengan threshold 2, 68.5% untuk gabungan dengan threshold 2 dan 93.2% untuk MobileNetV2 dengan threshold 1.5. Berdasarkan akurasi tersebut teknik ekstraksi fitur dengan model MobileNetV2 dapat merepresentasikan data lebih baik dibandingkan dengan ekstraksi skeleton, image, dan gabungan

Sign language is a series of movements that represent the vocabulary of a particular language and is designed to help the hearing-impaired communicate. However, not everyone is familiar with the sign language gestures, so a sign language translation system would aid communication by allowing more people to understand sign language gestures. A video that contains a sequence of sign sentences with two types of movements, namely sign movements (word-gestures) which have represent language constructs, and transitional movements (transitional-gesture). A method to identify both word-gestures and transitional-gestures in a variant of the Indonesian Sign Language System called Sistem Isyarat Bahasa Indonesia (hereafter referred to as SIBI) sentences based on the Threshold Conditional Random Field (TCRF) was implemented. The dataset on which the model is trained, consists of 2,255 videos containing recorded movements for 28 commonly used sentences in SIBI, performed by three teachers and two students of the Santi Rama School (Sekolah Luar Biasa), a school for hearing-impaired students. Several feature extraction techniques were tested, including skeleton, image, skeleton-image combination and MobileNetV2. The classification method uses TCRF with variations in TCRF threshold values between 1 to 4 to recognize word-gestures and transitional-gestures, then deleting frames with transitional-gestures label, and obtaining accuracy from LSTM that recognizes words from the per-frame word-gesture label. The best accuracies achieved by each method were 72.5% for skeleton technique with a TCRF threshold of 2; 70.3% for image technique with a TCRF threshold of 2; 68.5 % for skeleton-image combination, with a TCRF threshold of 2; and 93.2% for MobileNetV2 with threshold 1.5. Using MobileNetV2 as a feature extractor yields significantly better results than previous feature extraction methods.

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Depok: Fakultas Ilmu Komputer Universitas Indonesia, 2020
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UI - Tesis Membership  Universitas Indonesia Library
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Sandler, Wendy
Sign language and linguistic universals
New York: Cambridge University Press, 2006
419 SAN s
Buku Teks  Universitas Indonesia Library
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