Classification of handwritten Javanese script using random forest algorithm

Abstract

The ability to read and write Javanese scripts is one of the most important competencies for students to have in order to preserve the Javanese language as one of the Indonesian cultures. In this study, we developed a predictive model for 20 Javanese characters using the random forest algorithm as the basis for developing Javanese script learning media for students. In building the model, we used an extensive handwritten image dataset and experimented with several different preprocessing methods, including image conversion to black-and-white, cropping, resizing, thinning, and feature extraction using histogram of oriented gradients. From the experiment, it can be seen that the resulting random forest model is able to classify Javanese characters very accurately with accuracy, precision, and recall of 97.7%.