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Luky Agus Hermanto
Teknik Informatika, Fakultas Teknologi Informasi, Institut Teknologi Adhi Tama Surabaya
Computer Science & IT

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Adaptive Ant Colony Optimization on Mango Classification Using K-Nearest Neighbor and Support Vector Machine Febri Liantoni; Luky Agus Hermanto
Journal of Information Systems Engineering and Business Intelligence Vol. 3 No. 2 (2017): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (726.262 KB) | DOI: 10.20473/jisebi.3.2.75-79

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
Co-Authors Febri Liantoni