Prediksi Motif Batik dengan Menggunakan Metode Gabor Filter Convolution Neural Network - Bili, Yudisman Ferdian; Tundo,; Sutisna, Nandang; Putri, Atsilah Daini; Yuliantoro, Dita Tri; Nurmayanti, Laily (Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), 2025)
This research aims to develop a batik motif classification system by utilizing Convolutional Neural Network (CNN) and Gabor Filter, in order to increase accuracy in texture feature extraction. The batik dataset used goes through a preprocessing stage, which includes normalization and data augmentation. During training, the model was tested with 10,000 iterations, using the Adam optimizer and the Categorical Cross-Entropy loss function, and evaluated via a confusion matrix. Test results show accuracy reaching 87%, with a precision and recall value of 90% each, and an F1-score of 89%. This method has proven effective for classifying batik motifs and has the potential to be applied in the fields of education, textile industry and cultural preservation.
Keywords: CNN; Gabor Filter; Batik Classification; Deep Learning; Image Processing