Deteksi Keaslian Uang Kertas Berdasarkan Fitur Gray Level CoOccurrence Matrix (GLCM) Menggunakan K-Nearest Neighbor

Tamara, Defi and Anam, M. Haerul and Widari, Wike Sri and Falahudin, Ardan Venora and Oktavia, Widya Yuristika and Fitri, Zilvanhisna Emka and Arifianto, Aji Seto (2022) Deteksi Keaslian Uang Kertas Berdasarkan Fitur Gray Level CoOccurrence Matrix (GLCM) Menggunakan K-Nearest Neighbor. Jurnal Buana Informatika, 13 (2). pp. 105-115. ISSN 2089-7642

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Abstract

Rupiah is the currency of Indonesia. One form is rupiah banknotes. The issuance and circulation of rupiah banknotes are under the authority of Bank Indonesia (BI) as the central bank. Currently, many incidents of counterfeiting are troubling the public. One of the characteristics of the authenticity of money that has not yet been found in counterfeit money is invisible ink, which is an invisible print that can only be seen when the money is exposed to ultraviolet light. Behind it, prolonged exposure to ultraviolet light harms eye and skin health. A system for detecting the authenticity of banknotes was created to overcome these problems using image processing techniques. The research stages are literature study, collecting banknote images illuminated by ultraviolet light, image processing (rotation, cropping, and resizing), RGB color component solving, GLCM feature extraction, and classification using the k-Nearest Neighbor (KNN) method. The KNN method can classify the authenticity of banknotes with an accuracy of 88% using the values of K = 3 and 7.

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