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All Journal Bulletin of Electrical Engineering and Informatics
Yohanes Indra Riskajaya
Universitas Internasional Semen Indonesia
Electrical & Electronics Engineering

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Classification of handwritten Javanese script using random forest algorithm Mohammad Arif Rasyidi; Taufiqotul Bariyah; Yohanes Indra Riskajaya; Ayunda Dwita Septyani
Bulletin of Electrical Engineering and Informatics Vol 10, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i3.3036

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%.
Co-Authors Ayunda Dwita Septyani Mohammad Arif Rasyidi Taufiqotul Bariyah