Identifikasi Keaslian Uang Rupiah menggunakan Arsitektur MobilenetV3-Small Hybrid Berdasarkan Watermark
DOI:
https://doi.org/10.31004/joecy.v6i1.8055Keywords:
Batu saluran kemih, Obesitas, UrolithiasisAbstract
Peredaran uang palsu merupakan ancaman serius bagi stabilitas ekonomi di Indonesia, di mana kemajuan teknologi membuat pemalsuan semakin canggih dan sulit dideteksi secara manual. Penelitian ini bertujuan untuk merancang dan mengevaluasi model deep learning untuk mengidentifikasi keaslian uang Rupiah secara otomatis menerapkan arsitektur Convolutional Neural Network (CNN) MobileNetV3-Small model Hybrid, dengan fokus deteksi pada area watermark sebagai Region of Interest (ROI). Metodologi penelitian melibatkan pengumpulan 1600 citra dataset uang kertas pecahan Rp 50.000 dan Rp 100.000 emisi tahun 2016. Hasil pengujian menunjukkan bahwa konfigurasi hyperparameter optimal bervariasi untuk setiap pecahan. Model untuk pecahan Rp 100.000 mencapai performa terbaik dengan akurasi 84% dan loss 34% saat dilatih selama 100 epoch dengan learning rate 0.01 dan batch size 16. Model untuk pecahan Rp 50.000 menunjukkan performa terbaiknya dengan akurasi 78% dan loss 52% pada 50 epoch, learning rate 0.001, dan batch size 16.
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