Pengenalan Citra Tanda Tangan Off-Line dengan Pemanfaatan Ciri Centroid Distance Function - Rizka Husnun Zakiyyah; Agus Wahyu Widodo; Fitri Utaminingrum (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2017)
A person's signature is one of the most valid proof that shows ownership of documents and transactions that contain their most important data. However, the process of analizing its authenticity is still done manually. To resolve this problem, an image recognition system for signature will be developed by applying characteristic centroid distance function. This Image recognition process begins with preprocessing, such as binerisasi, filtering, cropping, resizing, and thinning. Next the position of pixels will be searched to store all the foreground pixels and centroid pixels of the image. All pixels stored distance will be calculated using centroid function and grouped according to the amount of features that were selected so that each group has the same amount of data. The average of centroid distance function will be counted on every group so that each group will generate one feature. The results of feature extraction will be processed with the k-nearest neighbor classification method. On the research that has been done the highest accuracy obtained from extraction characteristics of centroid distance function uses 20 class is 88.5% obtained from 20 features and k= 1 with the amount of 10 and 14 training data for each class. The highest accuracy to 50 class is 67.4% obtained from 15 features and k= 3 with 10 and 14 training data for each class.