(Department of Electrical Engineering and Informatic, Universitas Negeri Malang, Malang, Indonesia)(2) Reni Fatrisna Salsabila
(Department of Electrical Engineering and Informatic, Universitas Negeri Malang, Malang, Indonesia)(3) * Anik Nur Handayani
(Department of Electrical Engineering and Informatic, Universitas Negeri Malang, Malang, Indonesia)(4) Aji Prasetya Wibawa
(Department of Electrical Engineering and Informatic, Universitas Negeri Malang, Malang, Indonesia)(5) Emanuel Hitipeuw
(Department of Electrical Engineering and Informatic, Universitas Negeri Malang, Malang, Indonesia)(6) Kohei Arai
(Departemen of Information Science and Engineering, Saga University, Japan)*corresponding author
AbstractIndonesian Sign Language System (SIBI) recognition plays a crucial role in improving effective communication for individuals with hearing loss in Indonesia. To support automatic SIBI recognition, this research presents a performance analysis of two main algorithms, namely Decision Tree and C4.5, in the context of the SIBI recognition task. This research utilizes a rich SIBI dataset that includes a variety of SIBI signs used in everyday communication. Data pre-processing, model construction with both algorithms, and model performance evaluation using accuracy, precision, recall, and F1-score metrics are all part of the study. Regarding SIBI recognition accuracy, the experimental results demonstrate that the Decision Tree performs better than Decision Tree. The Decision Tree also makes models that are easier to understand, which is important for making communication systems based on SIBI. KeywordsIndonesian Sign Language System (SIBI) Decision Tree C4.5.
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DOIhttps://doi.org/10.31763/aet.v3i2.1536 |
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Copyright (c) 2024 Agil Zaidan Nugraha, Reni Fatrisna Salsabila, Anik Nur Handayani, Aji Prasetya Wibawa, Emanuel Hitipeuw, Kohei Arai
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