Klasifikasi Batik Parang Menggunakan Convolutional Neural Network (CNN)

Angginy Akhirunnisa Siregar
Citra Citra
Dechy Deswita Indriani.S
Gifari Dhaffa Prawira Sianturi

Populer: Jurnal Penelitian Mahasiswa
Vol. 3 No. 1 (2024): Maret : Jurnal Penelitian Mahasiswa

Batik culture is very strong in Indonesia, this is the reason that batik can be found throughout the archipelago, with unique characteristics that distinguish it in each region. However, people are often confused and find it difficult to recognize one type of batik from another. One of the famous types of batik motif is Batik Parang. This research aims to establish a Convolutional Neural Network (CNN) model to classify Batik Parang and help people distinguish it from other batik motifs. Deep learning, particularly CNN, was chosen because it has a high accuracy rate in image classification. A quantitative Experimental design is used, using a dataset of 100 batik images evenly divided into two classes, namely Batik Parang and not Batik Parang. The dataset is divided into two categories, namely training data and testing data, with a data ratio of 80:20. Thus, by using Convolutional Neural Network (CNN), the classification between Batik Parang and not Batik Parang produces an accuracy of 95%, with the use of epoch = 118 and batch_size = 100.