Batik Nitik 960 Dataset for Classification, Retrieval, and Generator

Agus Eko Minarno (), Indah Soesanti and Hanung Adi Nugroho ()
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Agus Eko Minarno: Department of Electrical and Information Technology, Jl. Grafika 2, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
Indah Soesanti: Department of Electrical and Information Technology, Jl. Grafika 2, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
Hanung Adi Nugroho: Department of Electrical and Information Technology, Jl. Grafika 2, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia

Data, 2023, vol. 8, issue 4, 1-10

Abstract: Batik is one of the traditional heritages of Indonesia, with each motif of batik having a profound cultural and philosophical significance. This article introduces Batik Nitik 960 dataset from Yogyakarta, Indonesia. The dataset was extracted from a piece of fabric with 60 Nitik patterns. The dataset was supplied by the Paguyuban Pecinta Batik Indonesia (PPBI) Sekar Jagad Yogyakarta collection of Winotosasto Batik and the data were extracted from the APIPS Gallery. Each of the 60 categories in the collection contains 16 photographs, for a total of 960 images. The photographs were acquired with a Sony Alpha a6400, illuminated with a Godox SK II 400, and the data were compressed using the jpg file format. Each category contains four motifs rotated by 90, 180, and 270 degrees. Thus, the total number of images per motif is 16. Each class has a specific philosophical significance associated with the motif’s origins. This dataset aims to enable the training and evaluation of machine learning models for classification, retrieval, or generation of a new batik pattern using a generative adversarial network. To our knowledge, this study is the first to present a Batik Nitik dataset equipped with philosophical significance that is freely accessible.

Keywords: batik; nitik; dataset; deep learning; classification; image retrieval; generative adversarial network (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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