Klasifikasi Gambar Aksara Jawa Menggunakan Optimalisasi Parameter SVM dengan Kernel Cosine

Aji  Priyambodo; Prihati  Prihati

Authors

Aji Priyambodo Institut Teknologi dan Bisnis Semarang
Prihati Institut Teknologi dan Bisnis Semarang

Keywords:

Javanese Script, Support Vector Machine, Cosine Similarity, Histogram of Oriented Gradients, Parameter Optimization

Abstract

Pattern recognition of Javanese script (Hanacaraka) plays a vital role in cultural preservation through digital technology. This study aims to develop a classification model for Javanese script images using Support Vector Machine (SVM) with a Cosine similarity kernel, supported by parameter optimization to enhance accuracy. A dataset of 4,385 images underwent preprocessing and feature extraction using Histogram of Oriented Gradients (HOG). SVM parameter optimization via GridSearchCV resulted in a significant accuracy improvement. The proposed model achieved a 99.84% accuracy, outperforming previous methods such as CNN-SVM and DCNN. This study demonstrates the effectiveness of Cosine similarity in Javanese script recognition and contributes to the advancement of machine learning-based classification systems.

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