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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Audrey Tilanov Pramasa
Udayana University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Extensive Deep Learning Models Evaluation For Indonesian Sign Language Recognition Audrey Tilanov Pramasa; Ni Putu Sutramiani; I Putu Agung Bayupati; I Wayan Agus Surya Darma
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 16 No. 02 (2025): Vol.16, No. 02 August 2025
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2025.v16.i02.p04

Abstract

Sign language is a vital communication method for individuals with hearing loss or deafness, with variations reflecting unique cultural contexts. Real-time recognition of sign language can bridge communication gaps, yet­­ developing tools for Indonesian Sign Language (BISINDO) is challenging due to limited datasets. This research addresses these challenges by enhancing BISINDO detection and real-time rec­­ognition, focusing on flexible dataset collection and adaptation to varying lighting conditions. Three convolutional neural networks—InceptionV3, MobileNetV2, and ResNet50—are evaluated with optimizers SGD, Adagrad, and Adam to determine the best architecture-optimizer combination. Models were trained on a common dataset and analyzed for optimal performance. Real-time recognition uses MobileNetV2 SSD, integrating data augmentation to improve performance under diverse lighting. The system was deployed on a mobile device for practical use. Results showed the real-time model attained a mean Average Precision (mAP) of 90.34%. This study demonstrates significant advancements in BISINDO recognition and real-time application
Co-Authors I Putu Agung Bayupati I Wayan Agus Surya Darma Ni Putu Sutramiani