HANDWRITING PREDICTION WITH LEARNING VECTOR QUANTIZATION METHOD IN MOBILE APPLICATION

Advances in technology are now increasing bringing people towards digital and mobile applications. To determine the owner of a handwriting, one of the manual techniques commonly used by humans that can be facilitated by mobile application technology is handwriting recognition. Learning Vector Quantization is one of the machine learning methods used to perform handwriting recognition and is one of the Artificial Neural Network (ANN) methods. This study aims to build a handwriting recognition system using the Learning Vector Quantization method on a mobile application, with feature extraction as the basic step in interpreting and classifying images. The results obtained from testing the prediction of learning vector quantization from 16 new data with a total of 80 tests. The results showed that 54 data were correct and 26 were incorrect, so the accuracy was 67.5%. Then obtained precision = 75.17%, recall = 67.5%, learning rate = 0.005, alpha value = 0.05 and iteration = 100