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All Journal Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Fikri Fakhruddin
Universitas Pembangunan Nasional Veteran Jawa Timur
Computer Science & IT

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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: Jurnal Teknologi Informasi dan Komunikasi Vol. 13 No. 1 (2023): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v13i1.37

Abstract

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.
Co-Authors Andreas Nugroho Sihananto Erista Maya Safitri Mochammad Ervinda Yudistira Yoga Maulana