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Journal : JOIV : International Journal on Informatics Visualization

Batik Classification using Microstructure Co-occurrence Histogram Agus Eko Minarno; Indah Soesanti; Hanung Adi Nugroho
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2152

Abstract

Batik Nitik is a distinctive form of batik originating from the culturally rich region of Yogyakarta, Indonesia. What sets it apart from other batik styles is its remarkable motif similarity, a characteristic that often poses a considerable challenge when attempting to distinguish one design from another. To address this challenge, extensive research has been conducted with the primary objective of classifying Batik Nitik, and this research leverages an innovative approach combining the microstructure histogram and gray level co-occurrence matrix (GLCM) techniques, collectively referred to as the Microstructure Co-occurrence Histogram (MCH).The MCH method offers a multi-faceted approach to feature extraction, simultaneously capturing color, texture, and shape attributes, thereby generating a set of local features that faithfully represent the intricate details found in Batik Nitik imagery. In parallel, the GLCM method excels at extracting robust texture features by employing statistical measures to portray the subtle nuances within these batik patterns. Nevertheless, the mere fusion of microstructure and GLCM features doesn't inherently guarantee superior classification performance. This research paper has meticulously examined many feature fusion scenarios between microstructure and GLCM to pinpoint the optimal configuration that would yield the most accurate results. The dataset used consists of 960 Batik Nitik samples, comprising 60 categories. The classifiers employed in this study are K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Naïve Bayes (NB), and Linear Discriminant Analysis (LDA). Based on the experimental results, the fusion of microstructure and GLCM features with the (LDA) classifier yields the best performance compared to other scenarios and classifiers.
Co-Authors Adha Imam Cahyadi Adhi Soesanto, Adhi Adhi Susanto Adhistya Erna Permanasari Afrisal, Hadha Agus Eko Minarno Agus Jamal Al-Fahsi, Resha Dwika Hefni Andrey Nino Kurniawan Andrey Nino Kurniawan Nino Kurniawan Andrey Nino Kurniawan, Andrey Nino Anna Nur Nazilah Chamim Aqil Aqthobirrobbany Aqthobirrobbany, Aqil Arief Rachma Wibowo Bambang Sutopo Bana Handaga Beta Estri Adiana Cepi Ramdani Chamim, Anna Nur Nazilah Danny Kurnianto Desyandri Desyandri Dewi Purnamasar Diah Priyawati Dian Nova Kusuma Hardani Domy Kristomo Dwi Rochmayanti Dwi Rochmayanti Dwi Rochmayanti Eka Firmansyah Elfrida Ratnawati Faaris Mujaahid Fathania Firwan Firdaus Fikri Zaini Baridwan Hanifah Rahmi Fajrin Hanung Adi Nugroho Hedi Purwanto Hendriyawan A., M. S. Henry Sulistyo Hidayatul Fitri Hotama, Christianus Frederick Husnul Rahmawati Sakinnah I Made Agus Wirahadi Putra Ikhwan Mustiadi Indriana Hidayah Isbadi Urifan Karisma Trinanda Putra, Karisma Trinanda Krisna Nuresa Qodri Litasari Litasari Litasari M.S. Hendriyawan Achmad Maesadji Tjokronagoro Maesadji Tjokronagoro Maesadji Tjokronegoro Medycha Emhandyksa Meirista Wulandari Muhamad Yusvin Mustar Muhammad Arzanul Manhar Muhammad Rausan Fikri Noor Akhmad Setiawan Nurokhim Nurokhim Oki Iwan Pambudi Oktoeberza, Widhia KZ Oyas Wahyunggoro Paulus Tofan Rapiyanta Pipit Utami Ramadoni Syahputra Ratnasari Nur Rohmah Rina Susilowati Risanuri Hidayat Rudy Hartanto Sekar Sari Siti Helmyati Soesanto, Adhi Sulistyo, Henry Sunu Wibirama Syahfitra, Febrian Dhimas Thomas Sri Widodo Thomas Sri Widodo Thomas Sri Widodo Thomas Sri Widodo Tole Sutikno Warsun Najib Widyawan Widyawati Prima, Widyawati Wijaya, Nur Hudha Wijaya, Nur Hudha Wiyagi, Rama Okta Yudhi Agussationo Yundari, Yundari