Signature Identification Menggunakan Metode Template Matching dan Fuzzy K-Nearest Neighbor

Abstract

Abstract — Signature is the result of the process of writing a person of a particular nature as a symbolic substance, which means a symbol or mark. Signature is usually used as an identifying mark of a person, each person must have his own signature in a different pattern. Because it's used as a person's identifying badge, Signatures now become particularly susceptible to counterfeiting and abuse that require check with a signature pattern recognition. This research has created a signature pattern recognition system using methods Template Matching and Fuzzy K-Nearest Neighbor to help recognize a person's signature pattern. The number of signatures used is 110 in two categories: the original signature with 100 data and the false signature with 10 data, and there were 10 classes taken using smartphone cameras. From this research, it was found that the best value from the image size of 200x200 pixels was 92% of the class that owned the signature legible, Positive Predictive Value (PPV) 88% and False Rejection Rate (FRR) 12%, with a k=3 on the original signature, and 90% of the class that owned the signature legible, Negative Predictive Value (NPV) 90% dan False Acceptance Rate (FAR) 10% with a k=9 on the false signature. From these results, it could be concluded that methods Template Matching and Fuzzy K-Nearest Neighbor could be used for signature pattern recognition.