Adaptive Ant Colony Optimization on Mango Classification Using K-Nearest Neighbor and Support Vector Machine

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ID: 11768
2017
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Abstract
Abstract— Leaves recognition can use an image edge detection method. In this research, the classification of mango gadung and manalagi will be performed. In the preprocess stage edge detection method using adaptive ant colony optimization method. The use of adaptive ant colony optimization method aims to optimize the process of edge detection of a mango leaves the bone image. The application of ant colony optimization method on mango leaves classification has successfully optimized the result of edge detection of a mango leaves the bone structure. Results showed edge detection using adaptive ant colony optimization method better than Roberts and Sobel method. The result an experiment of mango leaves classification with k-nearest neighbor method get accuracy value equal to 66,25%, whereas with the method of support vector machine obtained accuracy value equal to 68,75%. Keywords— Edge Detection, Ant Colony Optimization, Classification, K-Nearest Neighbor, Support Vector Machine
Reference Key
liantoni2017adaptivejournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Febri Liantoni;Luky Agus Hermanto;
Journal journal of information systems engineering and business intelligence
Year 2017
DOI
10.20473/jisebi.3.2.75-79
URL
https://e-journal.unair.ac.id/JISEBI/article/download/5600/3885
Keywords
edge detection ant colony optimization classification k-nearest neighbor support vector machine

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