Indonesian Sign Language Image Detection Using Convolutional Neural Network (CNN) Method - Andreas Nugroho Sihananto; Erista Maya Safitri; Yoga Maulana; Fikri Fakhruddin; Mochammad Ervinda Yudistira (Inspiration)
In Indonesia, there are two sign languages utilized by the deaf community, SIBI and BISINDO. Unfortunately, the majority of non-deaf individuals and deaf companions are not proficient in sign language. To address this communication gap, information systems can play a pivotal role in recognizing sign language speech. Recently, researchers conducted a study using the Convolutional Neural Network (CNN) algorithm to predict sign language for both SIBI and BISINDO datasets. The aim was to develop a model that could accurately translate sign language into written or spoken language, thus bridging the gap between deaf and non-deaf individuals. The research found that the CNN algorithm performed optimally on epoch 50 for SIBI with a testing accuracy of 93.29 %, while for BISINDO, it achieved the best result on epoch 40 with a testing accuracy of 82.32 %. These results suggest that the CNN algorithm has the potential to accurately recognize and translate sign language, thus improving communication between deaf and non-deaf individuals in Indonesia.