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All Journal Imajinasi JOIV : International Journal on Informatics Visualization
- Salamun
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PERSOALAN ISME DALAM SENI RUPA (DI) INDONESIA Salamun, -
Imajinasi Vol 5, No 1 (2009): Imajinasi
Publisher : Jurusan Seni Rupa, Fakultas Bahasa dan Seni, Universitas Negeri Semarang

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Abstract

Dalam sejarah seni rupa modern Indonesia, tedapat dua gerakan menonjol yang menjembatani perkembangan progresif dari seni murni yang dibawakan oleh PERSAGI (Persatuan Ahli Gambar Indonesia) dan kelompok GSRBI (Gerakan Seni Rupa Indonesia Baru Indonesia). PERSAGI mengusung semangat kebebasan dalam penciptaan ke arah munculnya gaya seni yang beragam. Sementara GSRBI mereformasi konvensi akademik seni rupa melalui berbagai  kemungkinan mengekspresikan dan memilih media secara bebas. Namun dari aspek produk kreasi, usaha-usaha mereka tidak sebaru sebagaimana pada kelompok seni rupa Barat yang telah memulai lebih dulu. Di Barat perkembangan seni rupa modern menghasilkan berbagai ”isme” silih berganti sementara di Indonesia tampaknya hanya sebatas pada pengadaptasian belaka.Kata kunci: gerakan, gaya, kreasi.
An Improved Okta-Net Convolutional Neural Network Framework for Automatic Batik Image Classification Elvitaria, Luluk; Ahmad, Ezak Fadzrin; Samsudin, Noor Azah; Ahmad Khalid, Shamsul Kamal; Salamun, -; Indra, Zul
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2591

Abstract

Batik is one of Indonesia's most important cultural arts and has received recognition from UNESCO. Batik has high artistic and historical value with a variety of patterns. Currently, Indonesia has 5,849 batik motifs which are generally classified based on shape, color, motif and symbolic meaning. The diversity of batik motifs makes it difficult for ordinary people to fully recognize them. This paper intends to develop an automatic framework for classifying batik motifs as a solution to overcome this issue. To develop this classification automation framework, the paper proposes a new architecture based on deep learning, which is named Okta-net. The architecture consists of 8 convolutional layers with separate convolution operations (SeparableConv2D). The output of the last convolution block will be fed to the fully connected layer using global average pooling. Meanwhile, in developing a deep learning model to classify batik image patterns, a dataset of 5 batik classes (motifs) was organized, consisting of 4,284 batik images. Through a series of experiments carried out, the proposed Okta-Net architecture succeeded in achieving satisfactory results with a validation accuracy of 93.17%, Precision of 91.60%, Recall of 92.28%, F-1 Score of 91.54%, and a loss of just 0.12%. Thus, it can be concluded that Okta-Net architecture can help preserve Indonesia's batik cultural heritage by accurate batik motif’s classification. Apart from that, based on a comparison of research outcomes, Okta-Net outperformed most of earlier studies, the majority of which had an accuracy of below 90%.
Co-Authors Ahmad Khalid, Shamsul Kamal Ahmad, Ezak Fadzrin Indra, Zul Luluk Elvitaria, Luluk Noor Azah Samsudin, Noor Azah