ANALISIS PENGARUH DIMENSI GAMBAR PADA KLASIFIKASI MOTIF BATIK DENGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK - Syefudin Syefudin; Muchamad Nauval Azmi; Gunawan Gunawan (Program Studi Sistem Informasi, Universitas Banten Jaya, 2023)
This study focuses on the classification of batik patterns using image data, specifically with three classes: kawung, parang, and pekalongan batik patterns. A total of 180 images were used for the research, divided equally among the three motifs. The dataset was collected through observations from Google. Data preprocessing involved two stages. In the first stage, the data was split into training and validation sets, with a 70% - 30% ratio, respectively. This separation allowed for evaluating the model's performance and generalization ability on unseen data. In the second stage, the Image Data Generator library was utilized to enhance data diversity and improve the model's ability to generalize patterns. Data augmentation techniques, such as rotation, shear, zoom, horizontal flip, and shift, were applied to the training data. The augmented data was then fed into a pre-defined Convolutional Neural Network (CNN) architecture. The CNN model processed the input data through convolutional layers with max pooling and ReLU activation functions. The outputs from the first convolutional layer were used as inputs for subsequent convolutional layers. The resulting feature maps were flattened and passed through fully connected layers for classification. The model's performance was assessed by evaluating accuracy measures. The CNN model achieved the highest accuracy of 98% on the training data and 100% on the validation data after 7 epochs.. The implementation was done using Python programming language and the Google Colab platform, along with required libraries. Model evaluation involved assessing the trained model's performance by inputting test data and computing evaluation metrics, including accuracy, precision, recall, and F1-score. Confusion matrix analysis provided insights into true positive, true negative, false positive, and false negative predictions. The classification report summarized the performance metrics of the model. In conclusion, the CNN method proved effective in classifying batik patterns. The size of the input images significantly influenced the accuracy of the model. A 148x148 pixel image size yielded 100% accuracy. As a suggestion, future research should consider using a larger dataset to maximize the accuracy of the classification model.