Analisis Perbandingan Metode LVQ Dan Backpropagation dalam Penentuan Keaslian Uang Kertas Rupiah Berbasis Parameter HSV - I Gusti Ayu Agung Diatri Indradewi; I Ketut Widhi Adnyana (Maranatha University Press, 2019)
The high demand of the community for money that causes crime is the circulation of fake paper money. Counterfeit money if shared has the same physical as the original money issued by Bank Indonesia. To avoid the public accidentally transacting using counterfeit money, the government has actually socialized the 3D method (Seen, Diraba, and Diterawang). However, along with technological developments, the technique of making counterfeit money will also require the development of alternative techniques that can be used to help save fake money. Determining the authenticity of Rupiah banknotes can be done using pattern classification methods, one of which can be accommodated by artificial neural networks. LVQ neural network (Learning Vector Quantization) and Backpropagation are two types of artificial neural networks that do supervised learning. Extraction of features that show the authenticity of banknotes can be done using the HSV color space. This color space consists of components H (Hue), S (Saturation), and V (Value). This is the background of the topic chosen for analysis choosing the LVQ and Backpropagation methods in determining the authenticity of Rupiah banknotes based on HSV parameters. Evaluation analysis taken from the level assessment The evaluation results using the composition of the test data consisting of 10 original money images and 8 original money images obtained results both LVQ and Backpropagation networks were able to classify real and fake money images with 100% acquisition rates. However, when viewed from the MSE value, the LVQ network has a better performance with the supporting MSE value being 0. The test results from the preparation time, the Backpropagation network requires a shorter time compared to the LVQ network.