Histogram of Gradient in K-Nearest Neighbor for Javanese Alphabet Classification

The Javanese script itself generally has a basic script or is also commonly referred to as the Carakan script. The script consists of 20 letters that have different levels of difficulty and some letters have similarities so that research is needed to make it easier to detect the image of Javanese characters. This study proposes a method of recognizing Hiragana's writing characters using the K-Nearest Neighbor (K-NN) method. In the preprocessing stage, the segmentation process is carried out using the thresholding method to perform segmentation, followed by the Histogram of Gradient (HOG) feature extraction process and noise removal using median filtering. Histogram of Gradient (HoG) is one of the features used in computer vision and image processing in detecting an object in the form of a descriptor feature. There are 1000 data divided into 20 classes. Each class represents one letter of the basic Javanese script. Data collection uses the writings of 50 respondents where each respondent writes 20 basic Javanese characters. From the results of the analysis of accuracy calculations, the highest accuracy was obtained at K = 1, namely 98.5%