<![CDATA[Pattern recognition is a discipline that aims to classify or describe objects based on their characteristics, quantitative measurements, or critical properties. Where a pattern is defined as an entity that is initially undefined, it can be identified and named through quantitative analysis. Pattern recognition can be applied to various fields, such as handwriting recognition, face recognition, eye recognition, skin, and image processing. One example of the application of pattern recognition is character recognition in letters used in learning. In this research, the digital image used as input comes from a two-dimensional image obtained through a digital camera. The digital image describes the light intensity in light and dark areas in the form of pixels and provides information about the object's color. To support the process of recognizing alphabet letters, which in this case are specifically Chinese alphabet letters, it will be assisted by using the Sokal Sneath Four Method. This significant mathematical approach helps create a compatible and accurate system for recognizing letter patterns through intensive data training. This method involves a series of steps, including data preprocessing, feature extraction, and classification, to train the system to recognize Chinese characters. The more training given to the system, the higher its accuracy in recognizing letter patterns, especially Chinese alphabet letters. The test results show that this Chinese alphabet letter pattern recognition system has a success rate of 65%, with a failure rate of 35%. Nevertheless, these results show room for further improvement in the algorithms used and the addition of training data to improve system performance and accuracy.]]>
