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All Journal Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)
A, Ibnu Al Razaq
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Computer Science & IT Decision Sciences, Operations Research & Management

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Deteksi Motif Batik Dengan Menggunakan CNN ResNet Untuk Mendeteksi Motif Pada Batik A, Ibnu Al Razaq; Wydyanto, W
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 2 (2025): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i2.933

Abstract

Batik is an Indonesian cultural heritage with diverse patterns and deep philosophical meanings. The advancement of artificial intelligence enables automatic recognition of batik motifs, supporting cultural preservation. This study develops a batik motif classification system using the Convolutional Neural Network (CNN) architecture, specifically ResNet18. The dataset consists of 1,427 images from 14 types of batik motifs, including Parang, Priangan, Pring-Sedawung, Kawung, and Megamendung. Preprocessing steps involved resizing images to 128x128 pixels and splitting them into training and testing sets across five scenarios. Experimental results indicate that the fourth scenario (60:40) achieved the best performance with 80% accuracy. These findings demonstrate that ResNet18 is effective for batik motif classification, although further improvements may be achieved with larger datasets and advanced augmentation techniques.
Co-Authors Wydyanto, W