Batik Motif Recognition Using the BGP-Model: A Hybrid GLCM-PCA Approach with Machine Learning Classifiers - Haryanto; Husin, Husna Sarirah (Engineering, Technology & Applied Science Research, 2026)
This study proposes the Batik-GLCM-PCA Model (BGP-Model), a hybrid feature-engineering framework that integrates the Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction with Principal Component Analysis (PCA) for dimensionality reduction. The BGP-Model was evaluated under two scenarios: classification using raw GLCM features and classification using PCA-reduced features, tested across three classifiers Naïve Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM). Experimental results demonstrate that the BGP-Model significantly improves classification performance, particularly for SVM (accuracy improved from 56.11% to 94.44%) and NB (from 78.33% to 85.00%). Although RF achieved its best performance (98.61%) with raw GLCM features, it experienced a slight decrease (93.89%) after dimensionality reduction. These findings confirm that the hybrid GLCM-PCA approach in the BGP-Model enhances classification accuracy, especially for algorithms sensitive to high-dimensional features, and contributes to the advancement of automated Batik motif recognition and the digitization of cultural heritage.