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All Journal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Bili, Yudisman Ferdian
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media Library & Information Science

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Pengenalan dan Edukasi Motif Batik Untuk Sekolah Dasar Negeri Pondok Bahar 06 Menggunakan Metode Convolution Neural Network (CNN) Bili, Yudisman Ferdian; Sarimole, Frencis Matheos
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 3 (2025): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v6i3.1573

Abstract

Batik is a cultural heritage of Indonesia rich in philosophical values and diverse motifs. However, a deep understanding of its meaning remains limited among elementary school students. This study aims to develop an educational application based on Convolutional Neural Networks (CNN) to introduce and classify batik motifs such as Kawung, Parang, Megamendung, and Truntum in an interactive manner. The batik image dataset was obtained from various online sources and underwent preprocessing, augmentation, training, and testing stages using the CNN model. The developed application was then tested with students from SD Negeri Pondok Bahar 06 using a pre-test and post-test method. Test results indicated that the CNN model was able to recognize batik motifs with adequate accuracy. Moreover, there was a significant improvement in students’ understanding of the philosophical meanings behind the motifs after using the application. Thus, integrating CNN technology into cultural learning proves to be effective in enhancing student interest and comprehension. This research is expected to serve as a reference for developing AI-based educational media to preserve local culture in the digital era.
Prediksi Motif Batik dengan Menggunakan Metode Gabor Filter Convolution Neural Network Bili, Yudisman Ferdian; Tundo; Sutisna, Nandang; Putri, Atsilah Daini; Yuliantoro, Dita Tri; Nurmayanti, Laily
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 3 (2025): JULI-SEPTEMBER 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i3.3798

Abstract

This research aims to develop a batik motif classification system by utilizing Convolutional Neural Network (CNN) and Gabor Filter, in order to increase accuracy in texture feature extraction. The batik dataset used goes through a preprocessing stage, which includes normalization and data augmentation. During training, the model was tested with 10,000 iterations, using the Adam optimizer and the Categorical Cross-Entropy loss function, and evaluated via a confusion matrix. Test results show accuracy reaching 87%, with a precision and recall value of 90% each, and an F1-score of 89%. This method has proven effective for classifying batik motifs and has the potential to be applied in the fields of education, textile industry and cultural preservation.
Co-Authors Nurmayanti, Laily Putri, Atsilah Daini Sarimole, Frencis Matheos Sutisna, Nandang Tundo Yuliantoro, Dita Tri