Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : JOIV : International Journal on Informatics Visualization

Preserving Indigenous Indonesian Batik Motif Using Machine Learning and Information Fusion Sumari, Arwin Datumaya Wahyudi; Aziza, Nadia Layra; Hani'ah, Mamluatul
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3714

Abstract

Preserving Indonesia’s indigenous cultural heritage in the form of Batik with various motifs to maintain the nation’s continuity from generation to generation. Hundreds of Batik motifs are spread across multiple regions of Indonesia, along with their unique names and meanings, where each motif has a cultural and historical meaning behind it. The distinctive patterns of Batik motifs challenge the community to remember and distinguish them, so it is crucial to have an intelligent system. This study designed and implemented a Batik motif classification system based on machine learning’s Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel. The primary key to classifier performance is features. An assessment was carried out on the performance of two feature models: single features and fused features. The Gray Level Co-occurrence Matrix (GLCM) produces the texture features of the Batik motif, and the Moment Invariant (MI) is used to create the shape features of Batik motifs. The Union Fusion and XOR operators produce a single fused feature of the two features. The proposed combination of techniques, namely SVM and GLCM, outperforms the combination scenario of Multi Texton Histogram (MTH), Multi Texton Co-Occurrence Descriptor (MTCD), Multi Texton Co-occurrence Histogram (MTCH) with SVM, and the combination of GLCM with 1-NN as well as the combination techniques that employed information fusion. The experiment results showed that the proposed combination technique achieved an accuracy of 97%. It can be concluded that SVM (RBF) with GLCM yields the best Batik motif recognition system.
Co-Authors Adhisuwignjo, Supriatna Aflah, Darin Zahira Agus Zainal Arifin Agus Zainal Arifin Akbar, Syafaat Alif Akbar Fitrawan Alif Akbar Fitrawan, Alif Akbar Ananta, Ahmadi Yuli Annisa Puspa Kirana Annisa Puspa Kirana Ariadi Retno Tri Hayati Ririd Arie Rachmad Syulistyo Arwin Datumaya Wahyudi Sumari Aryo Harto Aryo Harto Astrifidha Rahma Amalia Aziza, Nadia Layra Budi Harijanto, Budi Budiprasetyo, Gunawan Cahya Rahmad Candra Bella Vista Chastine Fatichah Christian Sri Kusuma Aditya Christian Sri Kusuma Aditya Christian Sri kusuma Aditya, Christian Sri kusuma Darin Zahira Aflah Deasy Sandhya E.I. Diana Purwitasari Diana Purwitasari Diana Purwitasari Dika Rizky Yunianto Dikky Rahmad Shafara Dwi Puspitasari Gunawan Budiprasetyo Hayati, Ariadi Retno Iftitah Hidayati Ika Kusumaning Putri Ika Kusumaning Putri Ilham Sinatrio Gumelar Imam Fahrur Rozi Irfan Thalib Alfarid Irsyad Arif Mashudi Komalasari, Nita Kusumaning, Ika Luqman Affandi Luqman Affandi M. Hasyim Ratsanjani Maulidia, Irma Moch Zawaruddin Abdullah Mochammad Hairullah Nita Komalasari Noprianto Noprianto Nurfaidah Nurfaidah Nurfaidah Nurfaidah Pratama, Muhammad Irgy Rahmad, Cahya Rahman, Muhammad Arif Rakhmat Arianto Rokhimatul Wakhidah Septianda Reza Maulana Shoumi, Milyun Ni’ma Sofyan Noor Arief Supriatna Adhisuwignjo Syafaat Akbar Triana Fatmawati Umi Laili Yuhana Vipkas Al Hadid Firdaus Vivi Nur Wijayaningrum Wilda Imama Sabilla Yan Watequlis Syaifudin Yogi Kurniaawan Yogi Kurniaawan, Yogi Yogi Kurniawan Yogi Kurniawan Yoppy Yunhasnawa