<![CDATA[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.]]>
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