Pengenalan ASL Menggunakan Metode Ekstraksi HOG dan Klasifikasi Random Forest - Ningrum Larasati; Siska Devella; Muhammad Ezar Al Rivan (Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP, 2021)
Sign languages ​​have many types, one of them is the American Sign Language (ASL). This study uses the ASL alphabet handshape image extracted with the Histogram of Oriented Gradient (HOG) feature and the resulting feature is used for the Random Forest classification. The test results show that using the HOG feature and the Random Forest classification method for ASL recognition gives a good accuracy rate, with an overall accuracy value of 99.10%, an average accuracy value per class of 77.43%, an average value of precision 88.81%, and an average recall value of 88.65%.