Hand Sign Recognition of Indonesian Sign Language System (SIBI) Using Inception V3 Image Embedding and Random Forest

Hand Sign Recognition of Indonesian Sign Language System (SIBI) Using Inception V3 Image Embedding and Random Forest

hand sign recognition;SIBI; Inception V3; image embedding; random forest

This paper presents a sign language recognition system for the Indonesian Sign Language System (SIBI) using image embeddings combined with a Random Forest classifier. A dataset comprising 5280 images across 24 classes of SIBI alphabet symbols was utilized.Image features were extracted using the Inception V3 image embedding, and classification was performed using Random Forest. Model evaluation conducted through K-Fold cross-validation demonstrated that the proposed methodachieved an accuracy of 85.40%, anF1score of 85.20%, a precision of 85.30%, and a recall of 85.40%. Moreover, the total computation time required by the proposed method is 1152.85 seconds. While the performance indicates room for improvement, this study lays the groundwork for enhancing sign language recognition systems to support the preservation and broader adoption of SIBI in Indonesia

Mayang Sari1, Eko Rudiawan Jamzuri2*

https://jurnal.iaii.or.id/index.php/RESTI/article/view/6156/1035

Departmentof Electrical Engineering, Politeknik Negeri Batam, Batam, Indonesia

21-03-2025

FAJAR BAGUS W

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