Deteksi Keaslian Uang Kertas Berdasarkan Fitur Gray Level Co-Occurrence Matrix (GLCM) Menggunakan K-Nearest Neighbor
DOI:
https://doi.org/10.24002/jbi.v13i02.5716Keywords:
Rupiah Banknotes, Authenticity of Money, Gray Level Co-occurrence Matrix, K Nearest NeighborAbstract
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.
Kata Kunci: Uang Kertas Rupiah, Keaslian Uang, Gray Level Co-occurrence Matrix, KNearest Neighbor
Abstrak. Rupiah merupakan mata uang Indonesia. Salah satu bentuknya adalah uang kertas rupiah. Penerbitan dan pengedaran uang kertas rupiah menjadi kewenangan Bank Indonesia (BI) sebagai bank sentral. Meski demikian, saat ini banyak kejadian pemalsuan uang yang meresahkan masyarakat. Salah satu ciri keaslian uang yang sampai saat ini belum ditemukan juga ada pada uang palsu ialah invisible ink, yaitu cetakan tidak kasat mata yang hanya terlihat ketika uang disinari cahaya ultraviolet. Dibalik hal itu, pancaran
sinar ultraviolet yang berkepanjangan rupanya berbahaya bagi kesehatan mata dan kulit. Untuk mengatasi permasalahan tersebut, dibuatlah sistem pendeteksi keaslian uang kertas yang memanfaatkan teknik image processing. Tahapan penelitian yaitu studi literatur, pengumpulan citra uang kertas yang disinari sinar ultraviolet, pengolahan citra (rotasi, cropping, dan resize), pemecahan komponen warna RGB, ekstraksi fitur GLCM, dan klasifikasi dengan metode k-Nearest Neighbor (KNN). Metode KNN mampu
mengklasifikasi keaslian uang kertas dengan perolehan akurasi 88% menggunakan nilai K = 3 dan 7.
Kata Kunci: Uang Kertas Rupiah, Keaslian Uang, Gray Level Co-occurrence Matrix, KNearest Neighbor
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