Optimasi decision tree menggunakan particle swarm optimization untuk identifikasi penyakit mata berdasarkan analisis tekstur

Optimizing decision tree using particle swarm optimization to identify eye diseases based on texture analysis

*Toni Arifin scopus  -  Department of Informatics, Universitas BSI Bandung, Indonesia
Asti Herliana  -  Department of Informatics, Universitas BSI Bandung, Indonesia
Received: 10 Aug 2019; Revised: 8 Nov 2019; Accepted: 18 Nov 2019; Published: 31 Jan 2020.
DOI: https://doi.org/10.14710/jtsiskom.8.1.2020.59-63 View
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Abstract
The problem of visual impairment is a serious problem with increasing cases, ranging from visual impairment to the cause of blindness. This study examines the development of an identification application for the classification of patients with eye disorders using the Decision Tree (DT) method, which is optimized using Particle Swarm Optimization (PSO). This study used 311 eye image data, consisting of 233 normal eye images and 78 eye images with glaucoma, cataracts, and uveitis. The feature extraction used Gray Level Co-occurrence Matrix (GLCM), while the feature optimization used the PSO and the learning method used DT. This optimized visual impairment classification application can improve system accuracy to 88.09 %.

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Keywords: optimization; classification; eye diseases, decision tree; particle swarm optimization; GLCM

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