Klasifikasi Motif Batik Nitik Berbasis Fitur Ekstraksi SqueezeNet dengan Reduksi Dimensi PCA–LDA - Suciani, Ratih; Sudibyo, Usman (Building of Informatics, Technology and Science (BITS), 2026)
Batik nitik motif classification faces significant challenges due to high intra-class variability and complexity of geometric dot patterns, along with limited samples per class in available datasets. Previous research using handcrafted feature extraction methods such as GLCM and MTCD achieved only 53% accuracy, while BSIF with data augmentation reached 97.70%. This study aims to develop a batik nitik classification method using feature extraction based on SqueezeNet trained on ImageNet to achieve superior accuracy without additional external data augmentation techniques. The Batik Nitik 960 dataset consisting of 960 images (60 classes × 16 samples) inherently contains natural visual diversity for each motif as curated by Minarno et al., enabling deep feature extraction from SqueezeNet to be optimized without extra augmentation. A 1000-dimensional feature vector extracted from SqueezeNet's pool10 layer then underwent dimensionality reduction using PCA, LDA, or PCA+LDA, and was classified with Random Forest, SVM, or KNN. These three classifiers were selected to represent distinct learning paradigms: ensemble method (Random Forest), margin-based classifier (SVM), and instance-based learning (KNN), enabling a comprehensive analysis of the extracted feature space characteristics. Experiments were conducted across various training data sizes (4-14 samples per class). Results showed that 8 out of 9 model combinations achieved perfect 100% accuracy, with LDA+SVM, LDA+KNN, PCA+LDA+SVM, and PCA+LDA+KNN requiring only 4 training samples per class. Only LDA+Random Forest failed to reach 100% (maximum 95.14%). The method's advantages lie in the deep feature extraction capability of SqueezeNet, which produces far more discriminative representations than handcrafted features, combined with the efficiency of supervised dimensionality reduction (LDA) in optimizing class separability. Inference time analysis shows that all model combinations are capable of performing predictions within the range of 0.013–0.173 ms per image, and stability evaluation using 5 random states confirms result consistency with mean accuracy ≥99.70% across 8 combinations (standard deviation ≤0.25%), confirming real-time implementation feasibility. This research establishes a new state-of-the-art for the Batik Nitik 960 dataset and opens opportunities for practical applications in authentication, quality control, and preservation of Indonesian batik cultural heritage. The primary contributions of this research encompass the application of SqueezeNet as a fixed feature extractor without fine-tuning for batik nitik classification a previously unexplored approach in this domain a comprehensive comparative analysis of nine dimensionality reduction and classifier combinations, and the establishment of a new state-of-the-art benchmark for the Batik Nitik 960 dataset, validating that CNN-based deep feature extraction surpasses handcrafted methods even with as few as four training samples per class. These findings pave the way for practical real-time batik identification systems applicable to authentication, quality control, and Indonesian cultural heritage preservation