Implementasi Deep Learning Menggunakan Metode Cnn Untuk Klasifikasi Jenis Ulos Batak Toba

Eka Fitrilia Sari Hutagalung
Pardomuan Sitompul

Student Scientific Creativity Journal
Vol. 1 No. 4 (2023): Juli : Student Scientific Creativity Journal

The Toba Batak tribe has a distinctive fabric known as ulos. Toba Batak ulos have types depending on their uses. But in the modern era, especially among urban communities, very few people know the types and uses. Motivated by the success of Convolutional Neural Network (CNN) algorithm in image classification, this study will conduct a learning-based approach to classify 5 types of Toba Batak ulos (Ragi Hidup, Ragi Hotang, Mangiring, Sadum, and Sibolang). The process starts from data collection, data analysis, model building, model training, and confusion matrix. The dataset used is 1000 images with 80% training data, 10% valid data, and 10% test data. Convolution, maxpooling, dropout, flatten, and fully connected are the 5 layers forming the CNN model. The optimizer used is Adam with a learning rate of 0.001. The model generated in this study can detect Toba Batak ulos images at an accuracy rate of 94.00%.