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.
Keywords: Pattern, Signature, Template Matching, Fuzzy K-Nearest Neighbor
