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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Applied Data Sciences
Maulana, Ahmad Rizki
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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Classification of Batik Motifs Using Multi-Texton Co-Occurrence Descriptor and Binarized Statistical Image Features Maulana, Ahmad Rizki; Suprapto, Suprapto; Tyas, Dyah Aruming
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.622

Abstract

This study aims to enhance the classification accuracy of batik motifs through a novel integration of Multi-Texton Co-Occurrence Descriptor (MTCD) and Binarized Statistical Image Features (BSIF). The primary objective is to develop a robust feature extraction method that effectively captures both textural and statistical properties of batik images, specifically utilizing the Batik Nitik 960 dataset. Our methodology employs a combination of MTCD and BSIF, followed by Principal Component Analysis (PCA) for dimensionality reduction, optimizing the model's ability to learn from diverse characteristics inherent in batik motifs to augment the diversity and robustness of the training data, we enhanced the Batik Nitik 960 dataset by applying vertical flipping, in addition to existing rotations. We explored three feature fusion approaches: Combination 1, where features are combined before normalization and PCA, achieving an accuracy of 99.948%; Combination 2, where normalization occurs prior to feature combination, also achieving an accuracy of 99.948%; and Combination 3, which applies PCA separately to each feature before combination, resulting in an accuracy of 99.896%. Experimental results demonstrate a remarkable accuracy in classifying these motifs, with the combined MTCD-BSIF features significantly surpassing the individual performances of MTCD at 95.729% and BSIF at 99.531%. This substantial improvement addresses the limitations identified in previous research, which reported an accuracy of only 0.71 on the same dataset. Furthermore, we explore the impact of various feature fusion techniques on classification performance, providing insights into the effectiveness of our proposed methods. Our findings suggest that the combined MTCD-BSIF approach can serve as a benchmark for future studies aiming to enhance classification accuracy in similar domains, thereby contributing to advancements in automated classification systems and their applications across various fields.
Optimizing nitik batik classification through comparative analysis of image augmentation Suprapto, Suprapto; Tentua, Meilany Nonsi; Maulana, Ahmad Rizki
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3970-3981

Abstract

Nitik batik is one of the most intricate and culturally significant motifs in Yogyakarta's batik tradition, characterized by its complex, geometric dot-based patterns. The unique challenges of automatically classifying nitik batik motifs stem from the high variability within the class and the limited availability of training data. This study investigates how different image data augmentation techniques can enhance the performance of a random forest classifier for nitik batik motifs. Techniques such as geometric transformations (flip, rotate, and scaling), intensity transformations (cut-out, grid mask, and random erasing), non-instance level augmentation (pairing samples), and unconditional image generation (deep convolutional generative adversarial network (DCGAN)) were used to expand the dataset and improve the model's ability to generalize. The results show that specific techniques, notably flip, cut-out, and DCGAN, significantly improved classification accuracy, with flip achieving the highest accuracy improvement of 20.20%, followed by cut-out at 19.27% and DCGAN at 16.25%. Moreover, DCGAN demonstrated the lowest standard deviation (0.78%), indicating high stability and robustness in classification performance across multiple validation folds. These findings suggest that augmentation techniques effectively improve classification accuracy and enhance the model's ability to generalize from limited and complex datasets.
Co-Authors Dyah Aruming Tyas Suprapto Suprapto Tentua, Meilany Nonsi