Verra Halizzah, - (2024) PENGEMBANGAN PENDETEKSI BAHASA ISYARAT BERBASIS WEBSITE DENGAN SUPERVISED LEARNING MENGGUNAKAN LIBRARY TENSORFLOW JS DAN REACT JS DI SLBN PURWAKARTA. S1 thesis, Universitas Pendidikan Indonesia.
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Abstract
Bahasa memiliki peran penting dalam komunikasi untuk menyampaikan informasi antarindividu yang umumnya komunikasi dilakukan secara verbal. Namun, tidak semua orang dapat melakukannya karena keterbatasan fisik, seperti gangguan pendengaran atau bicara. Penelitian ini dimaksudkan agar membantu komunikasi antara individu dengan keterbatasan fisik dan mereka yang tidak memiliki keterbatasan. Maka dari itu, penelitian ini merancang sistem pendeteksi bahasa isyarat dengan memanfaatkan Supervised Learning menggunakan model Machine Learning dan library React JS. Tujuan penelitian ini adalah menerapkan sistem deteksi bahasa isyarat serta menganalisis persepsi pengguna terhadap sistem yang telah dikembangkan. Metode penelitian yang digunakan adalah Research and Development (R&D) dengan model ADDIE. Sampel penelitian melibatkan siswa kelas 1 sampai 6 di SLBN Purwakarta. Pengujian sistem dilakukan dengan black box testing dan kuisioner. Hasil dari penelitian ini, sistem mampu mendeteksi 15 gestur BISINDO dan didapatkan tingkat akurasi dari model yang dibuat 93,8%. Analisis persepsi pengguna menunjukkan persentase sebesar 80,5% apabila berdasarkan kategori penilaian sebagian besar responden memberikan tanggapan positif. ----- Language plays an important role in communication to convey information between individuals, where communication is generally carried out verbally. However, not everyone can do so due to physical limitations, such as hearing or speech impairments. This research is intended to assist communication between individuals with physical limitations and those without. Therefore, this research designs a sign language detection system utilizing Supervised Learning, using Machine Learning models and the React JS library. The purpose of this research is to implement a sign language detection system and analyze user perceptions of the developed system. The research method used is Research and Development (R&D) with the ADDIE model. The research sample involved students from grades 1 to 6 at SLBN Purwakarta. System testing was carried out using black box testing and questionnaires. The results of this research show that the system can detect 15 BISINDO gestures, and the model achieved an accuracy rate of 93.8%. User perception analysis showed a percentage of 80.5%, indicating that the majority of respondents gave positive feedback based on the evaluation criteria.
| Item Type: | Thesis (S1) |
|---|---|
| Additional Information: | https://scholar.google.com/citations?hl=id&user=5LU0mNAAAAAJ ID SINTA Dosen Pembimbing Ahmad Fauzi: 6122861 Hafiyyan Putra Pratama: 6681148 |
| Uncontrolled Keywords: | Supervised Learning, React JS, Sistem Deteksi Bahasa Isyarat, Machine Learning Supervised Learning, React JS, Sign Language Detection System, Machine Learning |
| Subjects: | L Education > L Education (General) T Technology > T Technology (General) |
| Divisions: | UPI Kampus Purwakarta > S1 Sistem Telekomunikasi |
| Depositing User: | Verra Halizzah |
| Date Deposited: | 28 Aug 2024 03:44 |
| Last Modified: | 28 Aug 2024 03:44 |
| URI: | http://repository.upi.edu/id/eprint/121312 |
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