SISTEM DETEKSI UANG KERTAS RUPIAH MENGGUNAKAN METODE COLOR FILTERING HSV DAN ALGORITMA ORB (ORIENTED FAST ROTATED BRIEF)

    Sinta Agustina, - and Agus Heri Setya Budi, - (2025) SISTEM DETEKSI UANG KERTAS RUPIAH MENGGUNAKAN METODE COLOR FILTERING HSV DAN ALGORITMA ORB (ORIENTED FAST ROTATED BRIEF). S1 thesis, Universitas Pendidikan Indonesia.

    Abstract

    Uang merupakan alat tukar yang digunakan dalam kehidupan sehari-hari, namun masih sering ditemukannya uang palsu yang meresahkan masyarakat. Oleh karena itu, diperlukan sistem yang mampu mengenali dan memverifikasi uang kertas secara digital dan akurat. Tujuan dari penelitian ini yaitu untuk merancang sistem deteksi uang kertas rupiah berbasis pengolahan citra digital dengan menggunakan metode color filtering HSV untuk mendeteksi benang pengaman dan algoritma ORB (Oriented FAST Rotated BRIEF) untuk mengenali nominal uang kertas berdasarkan fitur visual. Sistem dikembangkan menggunakan bahasa pemrograman Python dengan antarmuka GUI berbasis Tkinter dan database SQLite sebagai media penyimpanan hasil deteksi. Data yang di uji sebanyak 700 citra uang. Pengujian dilakukan pada tujuh pecahan uang rupiah mulai dari Rp1.000 sampai Rp100.000 dengan berbagai kondisi pencahayaan dan rotasi. Hasil deteksi yang dilakukan menunjukkan bahwa metode HSV cenderung sensitif terhadap cahaya dan kondisi fisik uang, sedangkan metode ORB lebih stabil dalam mengenali ciri nominal uang. Sistem ini juga dilengkapi dengan fitur ekspor hasil ke Excel dan penyimpanan otomatis gambar snapshot. Secara keseluruhan, sistem mampu mendeteksi uang kertas dengan akurasi 89.29% untuk metode HSV dan 98.57% untuk metode ORB dengan rata-rata waktu deteksi sebesar 1,043 detik. Sistem ini dapat dijadikan dasar pengembangan aplikasi real-time dengan tambahan fitur suara untuk mendukung pengguna tunanetra dan kebutuhan transaksi digital. Money is a medium of exchange used in everyday life, but counterfeit money is still frequently found, which is disturbing the public. Therefore, a system is needed that is able to recognize and verify banknotes digitally and accurately. The purpose of this research is to design a rupiah banknote detection system based on digital image processing using the HSV color filtering method to detect security threads and the ORB (Oriented FAST Rotated BRIEF) algorithm to recognize banknote denominations based on visual features. The system was developed using the Python programming language with a Tkinter-based GUI interface and an SQLite database as a storage medium for detection results. The data tested were 700 banknote images. Tests were conducted on seven rupiah banknote denominations ranging from Rp1,000 to Rp100,000 with various lighting and rotation conditions. The detection results showed that the HSV method tends to be sensitive to light and the physical condition of the money, while the ORB method is more stable in recognizing the characteristics of the banknote denomination. This system is also equipped with a feature to export results to Excel and automatic saving of snapshot images. Overall, the system was able to detect banknotes with 89.29% accuracy for the HSV method and 98.57% for the ORB method, with an average detection time of 1.043 seconds. This system can be used as the basis for developing real-time applications with additional voice features to support visually impaired users and digital transaction needs.

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    Official URL: http://repository.upi.edu
    Item Type: Thesis (S1)
    Additional Information: https://scholar.google.com/citations?hl=id&authuser=1&user=8upCTvEAAAAJ SINTA ID : 6003446
    Uncontrolled Keywords: Uang; sistem deteksi; akurasi; color filtering HSV; algoritma ORB Money; detection system; accuracy; color filtering HSV; ORB algorithm
    Subjects: L Education > L Education (General)
    T Technology > TK Electrical engineering. Electronics Nuclear engineering
    Divisions: Fakultas Pendidikan Teknik dan Industri > Jurusan Pendidikan Teknik Elektro > Program Studi Teknik Tenaga Elektrik
    Depositing User: Sinta Agustina -
    Date Deposited: 22 Oct 2025 07:05
    Last Modified: 22 Oct 2025 07:05
    URI: http://repository.upi.edu/id/eprint/143272

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