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All Journal JAIS (Journal of Applied Intelligent System)
Mohammad Roni Kusuma
Dian Nuswantoro University

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The Involvement of Local Binary Pattern to Improve the Accuracy of Multi Support Vector-Based Javanese Handwriting Character Recognition Christy Atika Sari; Wellia Shinta Sari; Viki Ari Shelomita; Mohammad Roni Kusuma; Silfi Andriana Puspa; Muhammad Bima Gusta
Journal of Applied Intelligent System Vol 8, No 2 (2023): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i2.8450

Abstract

Indonesia is a country that is rich in cultural diversity. An example of one such variety is the Javanese language. The letters that are usually used in Javanese are non-Latin letters or are usually known as Javanese script. However, along with advances in technology, the Javanese language is increasingly being forgotten. In the past, the Javanese script was used as a subject in schools, aiming for Indonesian students to continue to gain knowledge about the Javanese script. The initial step in the introduction of the Javanese script starts with the preprocessing process by changing the image of the Javanese script from the RGB image to a grayscale image which is then performed feature extraction, where the feature extraction used in this script recognition is texture extraction with the Local Binary Pattern (LBP) algorithm. The results of this processing are obtained information that can be used as a parameter in the Multi Support Vector Machine (SVM) classification to predict Javanese script images. In this study using the LBP method with the Multi SVM Algorithm as a classification algorithm produces a high accuracy of 90% in the recognition of Javanese script, better than using only Multi SVM with an accuracy of 80%.
Co-Authors Christy Atika Sari Muhammad Bima Gusta Silfi Andriana Puspa Viki Ari Shelomita Wellia Shinta Sari