IMPLEMENTASI ARSITEKTUR MOBILENETV2 UNTUK KLASIFIKASI RUMAH ADAT BERBASIS ANDROID
DOI:
https://doi.org/10.26418/justin.v14i2.97977Keywords:
Rumah Adat NTT, Klasifikasi Gambar, MobileNetV2, React Native, Deep Learning, Aplikasi Mobile.Abstract
Rumah adat Nusa Tenggara Timur (NTT) merupakan warisan budaya yang kaya namun belum banyak terdokumentasi secara digital. Penelitian ini mengimplementasikan arsitektur MobileNetV2 berbasis transfer learning untuk mengklasifikasikan lima jenis rumah adat NTT, yaitu Bajawa, Ende, Pulau Timor, Sumba, dan Wae Rebo, menggunakan dataset sebanyak 1.125 citra yang dikumpulkan dari Google Images dengan pembagian 70% data latih dan 30% data uji. Untuk meningkatkan generalisasi model, diterapkan augmentasi data meliputi rotasi, pergeseran, zoom, dan penyesuaian kecerahan. Perbandingan dilakukan terhadap tiga arsitektur—MobileNetV2, MobileNetV3Small, dan ResNet50—dengan dua optimizer (SGD dan Adam), menggunakan konfigurasi epoch 50, batch size 32, dan learning rate 0,0001. Hasil evaluasi menunjukkan bahwa MobileNetV2 dengan optimizer SGD mencapai performa terbaik dengan akurasi, presisi, recall, dan F1-score masing-masing sebesar 0,98. Model ini kemudian dikonversi ke format TensorFlow Lite dan diintegrasikan ke dalam aplikasi Android berbasis React Native. Penelitian ini berkontribusi dalam menyediakan benchmark komparatif arsitektur deep learning ringan untuk klasifikasi citra budaya, sekaligus mendukung upaya digitalisasi dan pelestarian warisan budaya NTT melalui teknologi mobile.References
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