Implementasi Convolutional Neural Network (CNN) Untuk Pengenalan Tulisan Tangan Aksara Bima - Ahmad Angga Handoko; Mochamad Alfan Rosid; Uce Indahyanti (SMATIKA JURNAL : STIKI Informatika Jurnal, 2024)
Indonesia with its rich cultural diversity, certainly results in the existence of various languages in it. In the dialect of a region, there are letter symbols that represent the expression of the region's unique language. One example of a language that shows unique characteristics in its writing system is Bima, known as the Bima script. Bima script, or often referred to as Mbojo script, is a writing system traditionally used in the Bima region, located in West Nusa Tenggara Province. Bima script is still not widespread among the public, so it is important to preserve it as part of the cultural heritage of the Mbojo tribe. This research aims to train a computer to recognize Bima characters. The Convolutional Neural Network (CNN) method is used in this research to recognize Bima script handwriting. The dataset used consists of 2640 images of Bima script handwriting with 22 classes. The results showed a reliable performance of the CNN model, with an accuracy of 97,34%, precision 97,56%, recall 97,34%, and f1-score 97,31% on the test data.