Analisis Performa Convolutional Neural Arsitektur Mobile-NetV2 Untuk Deteksi Batik

Authors

  • DYA AYU SUCININGRUM Universitas Nusantara PGRI Kediri Indonesia
  • DANAR PUTRA PAMUNGKAS Universitas Nusantara PGRI Kediri Indonesia
  • DANANG WAHYU WIDODO Universitas Nusantara PGRI Kediri Indonesia

DOI:

https://doi.org/10.29407/hm241g29

Abstract

Keberagaman budaya Indonesia tecermin dalam motif batik. Mengingat kompleksitas identifikasi manual, penelitian ini mengembangkan sistem klasifikasi motif batik Kediri (Hewan, Tumbuhan, Wayang) menggunakan Convolutional Neural Network (CNN) berarsitektur MobileNetV2. Model dilatih dengan 600 gambar yang diproses dan di-augmentasi. Hasilnya, model mencapai akurasi validasi 90,50%, didukung metrik performa tinggi. Efisiensi komputasi MobileNetV2 menjadikannya solusi menjanjikan untuk aplikasi identifikasi batik real-time di perangkat mobile, sekaligus mendukung pelestarian warisan budaya

Keywords:

motif batik, cnn, Mobile-Netv2, Klasifikasi

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References

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Published

2025-07-10

Issue

Vol. 9 No. 2 (2025): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2025

Section

Articles

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Copyright (c) 2025 DYA AYU SUCININGRUM, DANAR PUTRA PAMUNGKAS, DANANG WAHYU WIDODO

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How to Cite

Analisis Performa Convolutional Neural Arsitektur Mobile-NetV2 Untuk Deteksi Batik. (2025). Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 9(2), 1499-1506. https://doi.org/10.29407/hm241g29