Classifying Hijaiyah Letters Handwritten Detection of Children Using CNN Algorithm - Ahmad Maulidi Roofiad; Annisa Sabillah; Aprian Nur Rohman; Elmi Wahyu Triyani (Asosiasi Khazanah Cendekia, 2024)
Learning the Hijaiyah letters is an important basis because in learning the Qur'an, these abilities must be mastered before they can be introduced and taught to children. However, the recognition of Hijaiyah letters in children's handwriting is still a challenge due to the variations and inconsistencies that are often found. Deep learning technology, particularly Convolutional Neural Network (CNN), has demonstrated its ability to classify letters with a high degree of accuracy. Therefore, this research aims to develop a CNN-based Hijaiyah letter classification model to help children learn to write and read Hijaiyah letters properly. This research uses a CNN model that is optimized with data augmentation techniques and hyperparameter tuning. The model was trained using a standard dataset totaling 1,740 samples of Hijaiyah letters. Model evaluation is done by calculating accuracy, precision, recall, and F1-Score on the validation dataset. The results showed that the proposed CNN model achieved almost 94.35% accuracy on the validation dataset. This research is expected to improve children's ability to learn Hijaiyah letters.