RANCANG BANGUN APLIKASI MOBILE PEMBELAJARAN HANACARAKA BALI MENGGUNAKAN METODE CNN BERBASIS CLOUD COMPUTING - Suwardana, I Nyoman; Jodi Acharya, Kadek; Dewanta Wiratama, Kadek Dwika; Sudarma, Made; Care Khrisne, Duman; Arsa Suyadnya, I Made (Jurnal SPEKTRUM; Vol 11 No 3 (2024): Jurnal SPEKTRUMDO - 10.24843/SPEKTRUM.2024.v11.i03.p7, 2024)
This research aims to develop a mobile application for learning Balinese Hanacaraka, designed to be engaging for students and to address the shortcomings of conventional learning methods, which are often less appealing. The application leverages Convolutional Neural Network (CNN) technology and cloud computing. TensorFlow is used for developing the CNN model, Android Studio for application development, and Google Cloud Platform (GCP) and Firebase for cloud services. The application offers four main features: writing practice, quizzes, statistics, and a dictionary. The CNN is divided into six models for Hanacaraka speech recognition, achieving an accuracy rate of 97-100% and an F1 score between 0.94-1. Testing results indicate that the application functions as expected, with a System Usability Scale (SUS) score of 70.57, suggesting user acceptance. API endpoints were tested using Postman and found to be reliable .