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Perancangan dan Implementasi Self-Checkout System pada Toko Ritel menggunakan Convolutional Neural Network (CNN)
Abstract
ABSTRAK
Perkembangan teknologi self-checkout system meningkatkan efektivitas dalam melakukan proses pembayaran. Self-checkout system merupakan fasilitas yang memungkinkan konsumen untuk melakukan pembayaran melalui scanning beberapa produk sekaligus dalam satu waktu dan pengemasan secara mandiri. Penelitian ini mengusulkan self- heckout system menggunakan Convolutional Neural Network (CNN) dan arsitektur model MobileNetV2 dengan metode hamming loss. Perancangan sistem diimplementasikan pada GUI sebagai user interface penelitian. Dataset yang digunakan pada penelitian ini berjumlah 247 citra dengan resolusi 224 x 224 pixels terhadap tiga jenis produk Teh Botol, Indomie dan Chitato. Hasil pengujian menunjukkan nilai akurasi deteksi sebesar 88.8% dengan hamming loss 0.12%. Posisi produk dalam keadaan berjarak dapat meningkatkan nilai akurasi. Sistem GUI pada penelitian ini berhasil mendeteksi produk hanya dalam waktu 1 detik.
Kata kunci: self-checkout system, convolutional neural network, MobileNetV2
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ABSTRACT
The development of self-checkout system technology increases the effectiveness of the payment process. Self-checkout system is a facility that allows consumers to make payments through scanning several products at the same time and packaging independently. This research proposes a self-checkout system using Convolutional Neural Network (CNN) and MobileNetV2 model architecture with hamming loss method. The system design is implemented on a GUI as a research user interface. The dataset used in this study amounted to 247 images with a resolution of 224 x 224 pixels of three types of bottled tea, Indomie and Chitato products. The test results show a detection accuracy value of 88.8% with a hamming loss of 0.12%. The position of the product in a spaced state can increase the accuracy value. The GUI system in this research successfully detects products in just 1 second.
Keywords: self-checkout system, convolutional neural network, MobileNetV2
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Abana, E. C., Daña, T. B., Alan, C., Martin, J. M., Buraga, R., & Balagtas, C. (2019). Self-Service Checkout System for Groceries. International Journal of Recent Technology and Engineering (IJRTE), 8(4), 1815–1818. https://doi.org/10.35940/ijrte.c6245.118419
Bukhari, S. T., Amin, A. W., Naveed, M. A., & Abbas, M. R. (2021). ARC: A Vision-based Automatic Retail Checkout System.
Hamim, M. (2018). Penggunaan Teknologi Berbasis RFID untuk Security System. Indonesian Journal Of Academic Librarianship, 2(2), 13–20.
Howard, A. G., Chen, B., & Wang, W. (2020). MobileNets : Efficient Convolutional Neural Networks for Mobile Vision MobileNets : Efficient Convolutional Neural Networks for Mobile Vision Applications. October.
Pathak, A. R., Pandey, M., & Rautaray, S. (2018). Application of Deep Learning for Object Detection. Procedia Computer Science, 132(Iccids), 1706–1717. https://doi.org/10.1016/j.procs.2018.05.144
Prabhu. (2018). Understanding of Convolutional Neural Network (CNN)-Deep Learning. Medium.
Prawira, I. M. R., Adiwijaya, A., & Mubarok, M. S. (2018). Klasifikasi Multi-Label Pada Topik Berita Berbahasa Indonesia Menggunakan Multinomial Naive Bayes. eProceedings of Engineering, 5(3), 7774–7781.
Rigner, A. (n.d.). AI-based machine vision for retail self-checkout system. GrosseryCheckout.
Rochman, F., & Junaedi, H. (2020). Implementasi Transfer Learning Untuk Identifikasi Ordo Tumbuhan Melalui Daun. Jurnal Syntax Admiration, 1(6), 672–679.
Sandler, M., Zhu, M., Zhmoginov, A., & Mar, C. V. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. IEEE/CVF Conference on Computer Vision and Pattern Recognition, 4510–4520.
Saputra, A. R. (2019). Simulasi Sistem Point of Sale Menggunakan Radio Frequency Identification Pada Perusahaan Ritel. JURNAL TEMATIKA, 7(1), 11–18.
Suartika, I. W., Wijaya, A. Y., & Soelaiman, R. (2016). Klasifikasi Citra Menggunakan Convolutional Neural Network ( Cnn ) pada Caltech 101. 5(1).
Sulaiman, A. W., Susanto, E., & Sunarya, U. (2016). Perancangan dan implementasi sistem faktur dan pembayaran otomatis pada toko swalayan berbasis rfid. Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika (TEKTRIKA), 1(1), 98–102.
Suwarno, & Lim, R. R. (2021). Perancangan Sistem Kasir Layanan Mandiri Berbasis Web Pada Supermarket Bless Dengan Metode Sdlc. Conference on Management, Business, Innovation, Education and Social Sciences (CoMBInES), 1(1), 860–873.
Tungadi, A. L., Lisangan, E. A., & Saputra, A. R. (2019). Simulasi Sistem Point of Sale Menggunakan Radio Frequency Identification Pada Perusahaan Ritel. TEKNOMATIKA, Jurnal Informatika dan Komputer, 12(1), 1–7.
Wang, W., Li, Y., Zou, T., Wang, X., You, J., & Luo, Y. (2020). A novel image classification approach via dense-mobilenet models. Mobile Information Systems, 2020. https://doi.org/10.1155/2020/7602384
Wiraguna, A., Faraby, S. Al, & Adiwijaya. (2019). Klasifikasi Topik Multi Label pada Hadis Bukhari dalam Terjemahan Bahasa Indonesia Menggunakan Random Forest. e-Proceeding of Engineering, 6(1), 2144–2153.
DOI: https://doi.org/10.26760/elkomika.v11i2.466
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ISSN (print) : 2338-8323 | ISSN (electronic) : 2459-9638
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