Rizal Adi Saputra, Jumadil Nangi, Ika Purwanti Ningrum, Muhamad Faza Almaliki, La Ode Rahmat Andre Pratama
{"title":"Deteksi Uang Palsu Rupiah dengan Menggunakan Metode Deteksi Tepi Laplacian of Gaussian (LoG) dan Algoritma K-Means Clustering","authors":"Rizal Adi Saputra, Jumadil Nangi, Ika Purwanti Ningrum, Muhamad Faza Almaliki, La Ode Rahmat Andre Pratama","doi":"10.24002/jbi.v13i02.5448","DOIUrl":null,"url":null,"abstract":"Abstract. Detection of Counterfeit Rupiah Using the Laplacian of Gaussian (LoG) Edge Detection Method and the K-Means Clustering Algorithm Counterfeit money is a severe problem that is increasing in every country. The reason is the ease of getting information on making counterfeit money and the development of technology such as color printers. This study used data from 20 images of authentic rupiah banknotes and 20 photos of fake rupiah banknotes. Data analysis in this study consisted of four stages: reading the image, converting the image to grayscale, image segmentation, and grouping image values. The dataset of real money images were taken with a cellphone camera, while counterfeit money images were obtained from the website. After the dataset retrieval process, the image conversion process was carried out into a grayscale image; then, the image segmentation process proceeded. The conclusion obtained from this study is that edge detection with Laplacian of Gaussian combined with the K-Means Clustering algorithm is quite effective in detecting an image to determine the picture as whether real money or counterfeit money.Keywords: Counterfeit Money, Laplacian of Gaussian, K-Means Clustering. Abstrak. Uang palsu adalah masalah serius yang semakin meningkat di setiap negara. Penyebabnya ialah kemudahan mendapatkan informasi cara pembuatan uang palsu serta perkembangan teknologi seperti printer warna. Penelitian ini menggunakan data 20 gambar uang kertas rupiah asli dan 20 gambar uang kertas rupiah palsu. Analisis data pada penelitian ini terdiri dari empat tahap, yaitu membaca gambar, mengubah gambar menjadi skala abu-abu, segmentasi gambar, dan pengelompokan nilai citra. Pengambilan dataset berupa uang asli dilakukan dengan kamera handphone dan gambar uang palsu didapatkan dari website. Setelah proses temu kembali dataset, dilakukan proses konversi citra menjadi citra grayscale, kemudian dilakukan proses segmentasi citra. Kesimpulan yang diperoleh dari penelitian ini adalah deteksi tepi dengan Laplacian of Gaussian yang dikombinasikan dengan algoritma K-Means Clustering cukup efektif mendeteksi suatu citra untuk menentukan gambar tersebut sebagai uang asli atau uang palsu.Kata Kunci: Uang Palsu, Laplacian of Gaussian, K-Means Clustering.","PeriodicalId":381749,"journal":{"name":"Jurnal Buana Informatika","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jurnal Buana Informatika","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24002/jbi.v13i02.5448","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Abstract. Detection of Counterfeit Rupiah Using the Laplacian of Gaussian (LoG) Edge Detection Method and the K-Means Clustering Algorithm Counterfeit money is a severe problem that is increasing in every country. The reason is the ease of getting information on making counterfeit money and the development of technology such as color printers. This study used data from 20 images of authentic rupiah banknotes and 20 photos of fake rupiah banknotes. Data analysis in this study consisted of four stages: reading the image, converting the image to grayscale, image segmentation, and grouping image values. The dataset of real money images were taken with a cellphone camera, while counterfeit money images were obtained from the website. After the dataset retrieval process, the image conversion process was carried out into a grayscale image; then, the image segmentation process proceeded. The conclusion obtained from this study is that edge detection with Laplacian of Gaussian combined with the K-Means Clustering algorithm is quite effective in detecting an image to determine the picture as whether real money or counterfeit money.Keywords: Counterfeit Money, Laplacian of Gaussian, K-Means Clustering. Abstrak. Uang palsu adalah masalah serius yang semakin meningkat di setiap negara. Penyebabnya ialah kemudahan mendapatkan informasi cara pembuatan uang palsu serta perkembangan teknologi seperti printer warna. Penelitian ini menggunakan data 20 gambar uang kertas rupiah asli dan 20 gambar uang kertas rupiah palsu. Analisis data pada penelitian ini terdiri dari empat tahap, yaitu membaca gambar, mengubah gambar menjadi skala abu-abu, segmentasi gambar, dan pengelompokan nilai citra. Pengambilan dataset berupa uang asli dilakukan dengan kamera handphone dan gambar uang palsu didapatkan dari website. Setelah proses temu kembali dataset, dilakukan proses konversi citra menjadi citra grayscale, kemudian dilakukan proses segmentasi citra. Kesimpulan yang diperoleh dari penelitian ini adalah deteksi tepi dengan Laplacian of Gaussian yang dikombinasikan dengan algoritma K-Means Clustering cukup efektif mendeteksi suatu citra untuk menentukan gambar tersebut sebagai uang asli atau uang palsu.Kata Kunci: Uang Palsu, Laplacian of Gaussian, K-Means Clustering.
摘要利用拉普拉斯高斯(LoG)边缘检测方法和k均值聚类算法检测印尼盾假币是各国日益严重的问题。原因是获取伪造假币的信息很容易,而且彩色打印机等技术的发展。本研究使用的数据来自20张真实印尼盾纸币的图像和20张假印尼盾纸币的照片。本研究的数据分析包括四个阶段:读取图像、将图像转换为灰度、图像分割、图像值分组。真实货币图像的数据集是用手机相机拍摄的,而假币图像则是从网站上获取的。数据集检索过程结束后,进行图像转换过程,得到灰度图像;然后,进行图像分割处理。本研究得出的结论是,利用拉普拉斯高斯聚类结合K-Means聚类算法进行边缘检测,可以有效地检测图像,判断图像是真钱还是假币。关键词:假币,拉普拉斯高斯,k -均值聚类,摘要黄帕素adalah masalah serius阳semakin脑膜炎设置negara。Penyebabnya ialah kemudahan mendapatkan信息系统,卡拉pembuatan wangpalsu serta perkembangan技术独立打印机警告。Penelitian ini menggunakan数据20 gambar wang kertas rupiia asli dan 20 gambar wang kertas rupiia palsu。分析数据:ini terdiri dari empat tahap, yitu membaca gambar, mengubah gambar, menjadi skala abu-abu, segmentasi gambar, dan pengelompokan nilai citra。彭甘比兰数据集berupa wong asli dilakukan dengan相机手机dan gambar wong palsu didapatkan dari网站。Setelah处理temu kembali数据集,dilakukan处理konversi citra menjadi citra灰度,kemudian dilakukan处理segmentasi citra。高斯聚类的拉普拉斯聚类算法,高斯聚类的拉普拉斯聚类,高斯聚类的拉普拉斯聚类,高斯聚类的拉普拉斯聚类,高斯聚类的拉普拉斯聚类,高斯聚类的拉普拉斯聚类,高斯聚类的拉普拉斯聚类,高斯聚类的拉普拉斯聚类,高斯聚类的拉普拉斯聚类,高斯聚类的拉普拉斯聚类,高斯聚类的拉普拉斯聚类,高斯聚类的拉普拉斯聚类,高斯聚类的拉普拉斯聚类,高斯聚类的拉普拉斯聚类,高斯聚类的拉普拉斯聚类,高斯聚类的拉普拉斯聚类,高斯聚类的拉普拉斯聚类,高斯聚类的拉普拉斯聚类。Kata Kunci, wang Palsu,高斯拉普拉斯,K-Means聚类。