Integrasi Model Deep Learning pada Pengembangan Aplikasi Android Pendeteksi Bahasa Isyarat SIBI Menggunakan Jetpack Compose - Raka Agus Maulana; Ahmad Rio Adriansyah; Zaki Imaduddin (DBESTI: Journal of Digital Business and Technology Innovation, 2026)
Communication barriers between the deaf community and the general public persist due to a limited understanding of sign language. This study aims to develop an Android application capable of recognizing static alphabet letters in the Indonesian Sign Language (SIBI) in real time to support inclusive communication. The application was developed using the Kotlin programming language and Jetpack Compose, and follows the Clean Architecture approach to ensure modularity and ease of maintenance. A machine learning model was integrated using TensorFlow Lite to enable optimal performance on mobile devices. The development process adopted the Rapid Application Development (RAD) methodology, including stages of literature review, system design, implementation, and testing. Black Box testing showed that all core features functioned as expected. Furthermore, user acceptance testing (UAT) showed a satisfaction rate of 91.3%, indicating a positive perception of usability and interface design. This application is expected to serve as an initial solution to help reduce communication gaps between the deaf community and the broader society.