Penerapan Metode Support Vector Machine untuk Pengenalan Pola Aksara Batak Toba

Abstract

The usage of the Batak Toba script has declined, and its complex forms pose challenges in pattern recognition. This study employs the Support Vector Machine (SVM) method to classify Batak Toba script patterns, utilizing a Histogram of Oriented Gradients (HOG) as a feature extraction technique. The data used comes from various sources, totaling 285 script images. After preprocessing, SVM was applied to separate characters into two main classes, which were further subdivided into subclasses until final classification was achieved. The results show that the combination of HOG and SVM can classify Batak Toba script characters with an accuracy of 89,47%. This research makes a significant contribution to the preservation of the Batak Toba script and has broader potential applications in pattern recognition and image classification.