Klasifikasi Aksara Sasak Menggunakan Convolutional Neural Networks (CNN)

Abstract

Sasak script is an important cultural heritage for the people of Lombok, but its use is decreasing along with the development of digital technology. This study aims to develop a classification system for Sasak script handwriting using Convolutional Neural Networks (CNN) to improve the accuracy of character recognition. The dataset used consists of handwritten images of 18 basic Sasak script characters collected from 50 volunteers with various writing styles. The methods applied include data preprocessing, augmentation, and training a CNN model with an architecture consisting of several convolutional and pooling layers. The results showed that the model achieved a validation accuracy of 92%, an average precision of 0.91, a recall of 0.89, and an F1-score of 0.90, indicating excellent performance in recognizing Sasak script characters. The conclusion of this study is that the developed system is not only effective in character recognition, but can also function as an interactive learning tool, supporting efforts to preserve Sasak script in the digital era. This research opens up opportunities for further development in the introduction of other traditional scripts.