Convolutional Neural Networks for Classification Motives and the Effect of Image Dimensions

Convolutional Neural Networks for Classification Motives and the Effect of Image Dimensions

Image Processing, Indonesian Batik Motifs, Convolutional Neural Networks

Although Indonesian batik patterns vary by location, they usually depict local customs and cultures. Each batik has a unique quality and,to correctlyidentify the batik designs, you need to understand the design patterns. However,many people struggle to identify and categorizethese kinds of motivationbecause they don't have the requisite knowledge, understanding, or access to sufficient information. This study used photo data to classify batikpatterns into 15different groups. Batik Kawung, Megamendung, Lasem, Pole, Machete, Gills, Nutmeg, Karaswasih, Cendrawasih, Geblek Renteng, Bali, Betawi, and Dayak are all included in this category. 1,350 images were used in the research. Google supports the collection of data. Toprovide the highest level ofprecisionand to evaluate how image dimensions affect the classification of batik designs, this study employs convolutional neural networks(CNNs). The results of this study show that Multi-Layer Perceptron (MLP) is a well-liked deep learning method for data classification, especially in domains where picture classification is involved. The size of the images utilizedaffects the accuracy ofcomputational neural network(CNN)algorithms. The resultsshowed that the testusing training datacomparisons of60%, 30%and 10%resulted in a 01.89% loss of 1.18%and a 100% improvement in accuracy

Siti Aisyah1, Rini Astuti2, Fadhil M Basysyar3 Odi Nurdiawan4, Irfan AlI

https://jurnal.iaii.or.id/index.php/RESTI/article/view/5623/910

Teknik Informatika, STMIKIKMI Cirebon, Cirebon, Indonesia2Sistem Informasi STMIK LIKMI Bandung, Bandung, Indonesia3Sistem Informasi STMIK IKMI Cirebon, Cirebon, Indonesia4Manajemen Informatika STMIK IKMI Cirebon, Cirebon, Indonesia5Rekayasa Perangkat Lunak STMIK IKMI Cirebon, Cirebon, Indonesia

22-02-2024

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