<![CDATA[In the fast-paced digital era, identity security has become crucial, and digital signatures play an important role in verification and authentication. This study focuses on the analysis and comparison of the performance of the K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms in digital signature pattern recognition. Both algorithms are widely used in classification tasks, and this study aims to identify which algorithm is most effective in recognizing and classifying digital signatures with the highest accuracy. Digital signature data was collected from various sources, including public datasets and directly collected data. Key features were extracted using the Gray-Level Co-occurrence Matrix (GLCM) method, which is effective in describing the texture and pattern of the signature. These features were used to train the KNN and SVM classification models. The performance of both algorithms was evaluated based on accuracy, precision, and recall metrics. The results showed that KNN with a value of k = 3 achieved an accuracy of 91.42%, while SVM with a linear kernel excelled with an accuracy of 97.06%. In addition, SVM is also more stable in handling complex signatures and has higher precision and recall than KNN, at 97.52% and 97.06%, respectively. On the other hand, KNN is faster in the training process and has a simpler implementation. This study provides valuable insights into the selection of optimal classification algorithms for digital signature recognition applications. The results of this study can be a guide for security and authentication system developers in choosing the most effective method to protect identity and prevent signature forgery.]]>
