An Effectivity Deep Learning Optimization Model to Traditional Batak Culture Ulos Classification

is one of the Batak culture's traditional heritage fabrics. Ulos cloth is divided into several types, each with a distinct function. Ulos Ragi Hotang, Ulos Pinunsaan, Ulos Tumtuman, Ulos Ragi Hidup, and Ulos Sadum are the five Batak ulos motifs. The Batak ulos motif has evolved over time and is now well-known in other countries. However, many ordinary people have difficulty distinguishing between ulos cloth and other fabrics. This study categorizes the different types of ulos cloth so that it can be used by ordinary people who are unfamiliar with the different types and functions. The Convolutional Neural Network is the method used (CNN). CNN is used to recognize and classify images. CNN's main feature is that it detects feature patches from locations in the input matrix and assembles them into high-level references. The Modular Neural Network (MNN) is then used to break down large and complex computational processes into smaller components, reducing complexity while still producing the desired output. 80% of the data for the training process, 20% for testing. The accuracy value achieved is 97.83%, the loss value is 0.0793, the val loss is 2.1885, and the val accuracy is 0.7429.

Automated summary

This study categorizes different types of Batak ulos cloth using Convolutional Neural Network (CNN) and Modular Neural Network (MNN) methods for image recognition and classification. 80% of the data was used for training, 20% for testing. The achieved accuracy is 97.83%, loss value is 0.0793, val loss is 2.1885, and val accuracy is 0.7429.