OPTIMIZING THE IMPLEMENTATION OF THE YOLO AND DATA ALGORITHM AUGMENTATION IN HANACARAKA JAVANESE SCRIPT LANGUAGE CLASSIFICATION

Abstract

The Javanese language and Javanese script are one of the rich cultural heritages in Indonesia, but there are still many people who do not have sufficient understanding of the Javanese language and culture, including the Javanese script. This can have an impact on the loss of language and cultural diversity, as well as the loss of the local identity of the Javanese people. The lack of knowledge about Javanese script language culture can be caused by various factors, such as the lack of access to adequate learning resources, the minimum use of Javanese language and script in daily life, and the lack of attention from the government and society towards the preservation of Javanese language and culture. This study aims to optimize the application of the YOLO (You Only Look Once) algorithm and data augmentation techniques in the Javanese language classification Hanacaraka script. The method used in this study was collecting data on Javanese Hanacaraka script images, data labeling, data augmentation, and model training using the YOLO algorithm. The results showed that the Javanese script pattern recognition method used the YOLO algorithm which had gone through the data augmentation process, showing good results with an accuracy of 96.4% using 3021 image data sources for the hanacaraka letters.