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IMPLEMENTASI DEEP LEARNING UNTUK DETEKSI REAL-TIME BAHASA ISYARAT SIBI MENGGUNAKAN BASIS ARSITEKTUR MOBILENETV2 BERBASIS ANDROID

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Penilaian

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This research focuses on implementing a deep learning model for real-time recognition of SIBI (Sistem Isyarat Bahasa Indonesia) sign language using MobileNetV2 architecture on the Android platform. The increasing need for communication tools to assist the deaf community motivates the development of a mobile application that translates SIBI hand gestures into readable information. MobileNetV2, optimized with transfer learning and TensorFlow Lite, ensures both computational efficiency and high accuracy for mobile devices. The resulting application detects and classifies the 26 alphabetic SIBI hand gestures in real-time, achieving outstanding performance with over 99% accuracy during testing. This research contributes to assistive technology development, offering a practical and accessible communication tool for the deaf community while advancing deep learning applications in mobile environments.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507000158	T163860	T1638602024	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1638602024

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xiii, 82 hlm., ilus., tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Informatika

Specific Detail Info

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Statement of Responsibility

EM

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