Analysis of Detecting the Authenticity of Money Using the Edge Detection Method

Nabella Putri Widianto; Kuat Indartono; Ade Nurhopipah

doi:10.35877/454RI.jinav2372

Nabella Putri Widianto Information Technology, Faculty of Computer Science, Amikom Purwokerto University, Indonesia (ID)
Kuat Indartono Information Technology, Faculty of Computer Science, Amikom Purwokerto University, Indonesia (ID)
Ade Nurhopipah Information Technology, Faculty of Computer Science, Amikom Purwokerto University, Indonesia (ID)

https://doi.org/10.35877/454RI.jinav2372

Keywords: Edge Detection, Fake Money, Histogram, Real Money, Texture Analysis.

Viewed = 0 time(s)

Abstract

One of the factors contributing to the increasing number of banknotes counterfeiting crimes is the technological advancements that have been made in the digital printing industry. Duplications that are very similar and even difficult to distinguish from the original banknote sheet are made easily due to high-quality duplication. Viewing, feeling, and looking carefully at a banknote is the traditional way to check the authenticity of a banknotes. And of course, due to the limitations of human capabilities, such methods are ineffective for many banknotes. Effectively and efficiently, edge detection can be completed by image processing or image processing methods. Using the camera, both real and fake banknotes are transferred. This results in two photos saved as JPG files representing both banknotes. After that, the conversion process to greyscale is carried out, edge observations are made on the image, and then a histogram of both images is created. The results of the histogram were compared and texture was analyzed as judged by the brightness and sharpness of the image.

Keywords: Edge Detection, Fake Money, Histogram, Real Money, Texture Analysis.

References

Akbar, M. (2022). KONSEP UANG DALAM ISLAM.

Akbar, S., Qisam, M. S., & Yasmin, G. A. (2023). Identifikasi Kanker Paru-Paru Menggunakan Metode Ekualisasi Histogram dan LBP. Journal of Electronics and Instrumentation, 1(1), 1–6.

Alfita, R., Ibadillah, A. F., & Prianto, A. (2022). Identifikasi Nilai Nominal Uang Kertas Berdasarkan Warna Berbasis Image Processing Menggunakan Metode Template Matching. Jurnal Teknik Elektro Dan Komputer TRIAC, 9(1), 1–5. https://doi.org/10.21107/triac.v9i1.12487

Bashori, A. H., Rosita, Y. D., & Makhfuddin, R. (2023). PENERAPAN METODE OCR DAN COLOUR FEATURE EXTRACTION UNTUK IDENTIFIKASI PELAT NOMORKENDARAAN. PROSIDING SEMASTEK, 2(1), 43–48.

Dihni, V. A. (2021, September 23). BI Temukan 188.370 Lembar Uang Palsu hingga Juli 2021. Databoks.

Fatimatuzzahro, S., & Yuliantari, R. V. (2021). Peningkatan Kualitas Citra pada Foto Sejarah Menggunakan Metode Histogram Equalizationdan Intensity Adjustment. JOURNAL OF APPLIED ELECTRICAL ENGINEERING, 5(2), 36–42.

Pambudi, A. R., Garno, & Purwantoro. (2020). DETEKSI KEASLIAN UANG KERTAS BERDASARKAN WATERMARK DENGAN PENGOLAHAN CITRA DIGITAL. JIP (Jurnal Informatika Polinema), 6(4), 69–74. https://doi.org/10.33795/jip.v6i4.407

Ptr, A. F. L., Rosnelly, R., Junaidi, & Amrullah. (2023). Modeling Digital Image Segmentation with Canny Method. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(2).

Puspita, N. (2020). APLIKASI PENDETEKSI KELAYAKAN PENUKARAN UANG KERTAS RUPIAH MENGGUNAKAN FITUR CANNY EDGE DETECTION, FITUR GLCM, DAN FITUR HISTOGRAM HSV MENGGUNAKAN METODE KLASIFIKASI SUPPORT VECTOR MACHINE (SVM) UNTUK PERANGKAT ANDROID.

Ratna, S. (2020). PENGOLAHAN CITRA DIGITAL DAN HISTOGRAM DENGAN PHYTON DAN TEXT EDITOR PHYCHARM. Technologia: Jurnal Ilmiah, 11(3), 181–186.

Ridwan, Sitorus, S. H., & Midyanti, D. M. (2020). Penerapan Metode Edge Detection Kirsch dan Robinson Untuk Mendeteksi Keaslian Uang Kertas Rupiah. Coding: Jurnal Komputer Dan Aplikasi, 8(1), 23–33. https://doi.org/10.26418/coding.v8i1.39190

Sani, K., Sani, W. W., & Fauziati, S. (2016). Analisis Perbandingan Algoritma Classification Untuk Authentication Uang Kertas (Studi Kasus: BanknoteAuthentication). Jurnal Informatika, 10(1), 1130–1139.

Sumardijanto, Sucitra, I. B., & Subanidja, S. (2023). Strategi Preventif Pencegahan Peredaran Uang Palsu di Indonesia. Innovative: Journal Of Social Science Research, 3(5), 9744–9755. https://doi.org/10.31004/innovative.v3i5.6050

Wishnuadji, T. W., & Narendro, A. (2019). ANALISA DETEKSI UANG PALSU MENGGUNAKAN METODE PEMROSESAN CITRA DIGITAL DENGAN DETEKSI TEPI DAN HISTOGRAM. Prosiding: SEMINAR NASIONAL INOVASI TEKHNOLOGI (SNITek), 2, 62–67.

Zulfiansyah, A. D. K., Kusuma, H., & Attamimi, M. (2023). Rancang Bangun Sistem Pendeteksi Keaslian Uang Kertas Rupiah Menggunakan Sinar UV dengan Metode Machine Learning. JURNAL TEKNIK ITS, 12(2), 166–173.