Performance Comparison of Machine Learning Algorithms for Ikat Weaving Classification - Hidajat, Moch. Sjamsul; Wibowo, Dibyo Adi; Mintorini, Ery (Universitas Muhammadiyah Malang, 2025)
Ikat weaving is a rich traditional heritage of Kota Kediri, Indonesia, with a diverse array of intricate motifs that reflect the cultural richness of the region. As new motifs emerge and information about older designs fades, manual identification becomes time-consuming and difficult. This study leverages machine learning technology, specifically XGBoost, Random Forest, and Neural Network algorithms, to automate the classification of these weaving patterns. The dataset consisted of 600 images, split into 480 images (80%) for training and 120 images (20%) for testing, representing four distinct weaving motifs: