Boosting CNN Accuracy for Sundanese Script Recognition through Feature Extraction Techniques - Pradana, Musthofa Galih; Khoirunnisa, Hilda (Journal of Applied Informatics and Computing, 2025)
Sundanese script is included in the cultural heritage in Indonesia, especially the culture in West Java. As a society that appreciates and preserves Indonesian culture and art, active participation can be realized through efforts to strengthen and preserve this script, one of which is by utilizing digital media. One of the technology-based digital media that can be used to preserve culture is image detection to make it easier to recognize Sundanese script. One of the models that can be used is the Convolutional Neural Network (CNN) with the MobileNetV2 architecture, with limited resources this architecture is able to produce good detection. This study applies the Convolutional Neural Network (CNN) algorithm with the MobileNetV2 architecture which will be tested with two main test scenarios, namely by applying feature extraction and without using feature extraction. The focus of this study will explore the influence and significance of the influence of feature extraction on the final results of image detection using the Convolutional Neural Network (CNN). The two feature extraction models used are Local Binary Pattern and Gray-Level Co-occurrence Matrix. These two feature extraction models will be tested with Sundanese script image data with data of 2,300 Sundanese script images. The results of this study show that the best results were obtained in the Convolutional Neural Network (CNN) with Gray-Level Co-occurrence Matrix (GLCM) with the best accuracy results at 93.8%. This is because the addition of the Gray-Level Co-occurrence Matrix (GLCM) is able to capture spatial texture statistics such as contrast, homogeneity, entropy, and correlation between pixel pairs. With these results, it can be concluded that in this study feature extraction has an effect and is able to increase the detection accuracy of the Convolutional Neural Network (CNN) model with the MobileNetV2 architecture in Sundanese script image data.