PENERAPAN CONVOLUTIONAL NEURAL NETWORK UNTUK PENGENALAN BAHASA ISYARAT INDONESIA: STUDI KASUS DAN TINJAUAN FILSAFAT SAINS

Hernalom Sitorus (Universitas Komputer Indonesia)
Ucu Nugraha (Universitas Komputer Indonesia)
Sri Titi Handayani (Universitas Komputer Indonesia)
Agus Nursikuwagus (Universitas Komputer Indonesia)
Usep Mohamad Ishaq (Universitas Komputer Indonesia)
Andrias Darmayadi (Universitas Komputer Indonesia)

JITTER (Jurnal Ilmiah Teknologi Informasi Terapan)
Vol. 12 No. 2 (2026)

The low literacy of Indonesian Sign Language (BISINDO) in the general public remains a barrier to communication with the Deaf community, while research on AI-based sign language recognition generally focuses solely on technical achievements. This study aims to develop a BISINDO alphabet recognition system based on Convolutional Neural Network (CNN) and evaluate it through a philosophy of science perspective. The methods used include collecting a BISINDO alphabet hand image dataset, data augmentation, and transfer learning-based CNN training with the MobileNetV2 architecture and a stepwise training scheme, then deployed to Android using TensorFlow Lite. Test results show the system is able to achieve an accuracy of around 93% on controlled test data with stable real-time inference performance. The scientific contribution of this research is not only in the development of applied AI systems, but also in providing a reflective ontological, epistemological, and axiological framework to assess the validity and social implications of BISINDO recognition technology.