Perbandingan Model Deep Learning Untuk Prediksi Klasifikasi Jenis Batik
DOI:
https://doi.org/10.22441/incomtech.v14i2.19651Keywords:
Batik, Deep Learning, KlasifikasiAbstract
Keanekaragaman budaya menjadi identitas bangsa Indonesia, keanekaragaman budaya sebagai bagian landasan dalam membangun identitas bagi bangsa Indonesia. Pemanfaatan batik menjadi salah satu bagian warisan budaya Indonesia adalah menjadi bagian dalam membangun nation brand. Tidak semua penduduk Indonesia mengingat berbagai motif batik yang beraneka ragam dan dengan harapan generasi muda turut menjaga dan selalu mencintai batik sebagai budaya bangsa. Pada penelitian ini membandingkan model prediksi klasifikasi citra dengan algoritma deep learning. Objek dari kajian ini adalah motif batik yang bersumber dari dataset citra batik. Tujuan dari penelitian ini, mengidentifikasi algoritma deep learning yang cocok dalam membuat model untuk mengklasifikasikan 15 jenis motif batik. Tahapan metode penelitian yakni analisa pemahaman terhadap terhadap masalah pengklasifikasian motif batik. Pengambilan data citra diambil dari publikasi dataset batik berupa data citra. Selanjutnya proses beberapa arsitektur algoritme deep learning yakni Simple CNN, RESNET50 V2, VGG16, MobileNet dan Inception V3. Pengukuran evaluasi menggunakan metric akurasi dan MSE untuk mendapatkan model arsitektur dengan hasil yang terbaik. Hasilnya diperoleh dengan tingkat akurasi terbaik pada algoritme RESNET50 V2 sebesar 86,36% dan memiliki nilai error MSE sebesar 0,0151. Kontribusi dari penelitian ini adalah model klasifikasi menggunakan algoritme deep learning CNN dengan arsitektur RESNET50 V2 direkomendasikan bagi pengembang sistem aplikasi ataupun device dalam mengklasifikasi 15 jenis motif batik.
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