Identification of Indonesian Rupiah Paper Currency Denominations Using First Order Statistical Feature Extraction and k-Nearest Neighbor

The rupiah currency is legal tender in Indonesia and issued by Bank Indonesia. The paper rupiah currency has undergone many changes. Although each paper rupiah has its characteristics, errors can occur when distinguishing the value of paper rupiah between denominations. With the rapid development of image processing that can be utilized in human life, the problem of errors in distinguishing the value of paper rupiah can be overcome with image processing techniques, where paper rupiah data can be identified using these techniques. This study focuses on the feature extraction and classification process. The dataset used in this study is the image of the paper rupiah with the 2022 emission. The image extraction process uses First-Order Statistical Characteristic Extraction to obtain the characteristics of each image object. In addition, K-Nearest Neighbor (KNN) is used to classify paper rupiah denominations. The accuracy, sensitivity, and specificity values are calculated to indicate the level of success of the test data compared to the training data. The testing process is carried out with k values of 1, 3, 5, and 7 on the front and back sides of the currency for all datasets. The highest accuracy value was obtained when k = 3. This test produced an average Accuracy value of 92.08%, an average Sensitivity value of 64.22%, and an average Specificity value of 96.61%. This research can be developed for more affordable counterfeit money detection, such as smartphone applications or portable devices that the general public can use.