Gradient based ant spread modification on ant colony optimization method for retinal blood vessel edge detection

Abstract. Leaf is one important part of a plant normally used to classify the types of plants. The introduction process of mango leaves of mangung and manalagi mango is done based on the leaf edge image detection. In this research the conventional edge detection process was replaced by ant colony optimization method. It is aimed to optimize the result of edge detection of mango leaf midrib and veins image. The application of ant colony optimization method successfully optimizes the result of edge detection of a mango leaf midrib and veins structure. This is demonstrated by the detection of bony edges of the leaf structure which is thicker and more detailed than using a conventional edge detection. Classification testing using k-nearest neighbor method obtained 66.67% accuracy. Keywords: edge detection, ant colony optimization, classification, k-nearest neighbor. Abstrak. Pengembangan Metode Ant Colony Optimization Pada Klasifikasi Tanaman Mangga Menggunakan K-Nearest Neighbor. Daun merupakan salah satu bagian penting dari tanaman yang biasanya digunakan untuk proses klasifikasi jenis tanaman. Proses pengenalan daun mangga gadung dan mangga manalagi dilakukan berdasarkan deteksi tepi citra struktur tulang daun. Pada penelitian ini proses deteksi tepi konvensional digantikan dengan metode ant colony optimization. Hal ini bertujuan untuk optimasi hasil deteksi tepi citra tulang daun mangga. Penerapan metode ant colony optimization berhasil mengoptimalkan hasil deteksi tepi struktur tulang daun mangga. Hal ini ditunjukkan berdasarkan dari hasil deteksi tepi citra struktur tulang daun yang lebih tebal dan lebih detail dibandingkan menggunakan deteksi tepi konvensional. Pengujian klasifikasi dengan metode k-nearest neighbor didapatkan nilai akurasi sebesar 66,67%.Kata Kunci: deteksi tepi, ant colony optimization, klasifiaksi, k-nearest neighbor.

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

Journal of Information Systems Engineering and Business Intelligence ◽

10.20473/jisebi.3.2.75-79 ◽

2017 ◽

Vol 3 (2) ◽

pp. 75 ◽

Cited By ~ 3

Author(s):

Febri Liantoni ◽

Luky Agus Hermanto

Keyword(s):

Support Vector Machine ◽

Edge Detection ◽

Ant Colony Optimization ◽

Nearest Neighbor ◽

Optimization Method ◽

Ant Colony ◽

Support Vector ◽

K Nearest Neighbor ◽

Edge Detection Method ◽

Mango Leaves

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

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