Eksperimen Pemodelan dan skenario Skenario Faster R-CNN untuk Penerapan Self Checkout Cashier

Manatap Dolok Lauro; Lina; Billy Marcelino; Lorico Salim

doi:10.37859/jf.v14i3.7596

Manatap Dolok Lauro Universitas Tarumanagara
Lina Universitas Tarumanagara
Billy Marcelino Universitas Tarumanagara
Lorico Salim Universitas Tarumanagara

https://doi.org/10.37859/jf.v14i3.7596

Keywords: self-checkout, computer vision, Faster R-CNN, pengenalan produk, toko kelontong

Abstract

Penelitian ini bertujuan untuk mengembangkan sistem self-checkout yang efisien dan hemat biaya untuk toko kelontong dengan memanfaatkan teknologi computer vision. Sistem yang diusulkan menggunakan Faster R-CNN, sebuah algoritma deep learning untuk mendeteksi dan mengenali produk secara real-time dari gambar yang diambil oleh kamera. Metode ini menghilangkan kebutuhan akan pemindaian barcode/QR code atau penggunaan tag RFID, sehingga mengurangi biaya operasional secara signifikan. Penelitian ini menggunakan 2.526 foto produk yang terdiri dari 10 kelas produk yang berbeda. Data ini dibagi menjadi data latih (2.026 foto) dan data uji (500 foto). Model Faster R-CNN dilatih menggunakan data latih dan kemudian dievaluasi kinerjanya menggunakan data uji. Hasil evaluasi menunjukkan bahwa model mampu mencapai akurasi hingga 84% ketika mendeteksi satu objek dalam satu frame. Namun, akurasi menurun menjadi 44% untuk tiga objek dan 12% untuk lima objek dalam satu frame. Meskipun terdapat penurunan akurasi pada skenario dengan banyak objek, penelitian ini menunjukkan potensi besar dari teknologi computer vision dalam meningkatkan efisiensi dan pengalaman berbelanja di toko kelontong. Penelitian lebih lanjut diperlukan untuk meningkatkan akurasi model dalam mendeteksi banyak objek secara bersamaan.

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