scholarly journals Pengembangan Metode Ant Colony Optimization Pada Klasifikasi Tanaman Mangga Menggunakan K-Nearest Neighbor

2017 ◽  
Vol 8 (4) ◽  
Author(s):  
Febri Liantoni ◽  
Luky Agus Hermanto
Keyword(s):  
Edge Detection ◽  
Ant Colony Optimization ◽  
Nearest Neighbor ◽  
Optimization Method ◽  
Ant Colony ◽  
K Nearest Neighbor ◽  
Edge Image ◽  
Image Detection ◽  
Mango Leaves ◽  
Leaf Midrib

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.

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

2017 ◽  
Vol 3 (2) ◽  
pp. 75 ◽  
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

Image Edge Detection Based on Ant Colony Optimization Algorithm

2016 ◽  
Vol 8 (1) ◽  
pp. 1-12
Author(s):  
Yin Huan
Keyword(s):  
Edge Detection ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Detection Methods ◽  
Matlab Simulation ◽  
Ant Colony Optimization Algorithm ◽  
Edge Image ◽  
Noise Interference ◽  
Image Edge Detection ◽  
Image Edge

Ant colony optimization (ACO) is a new heuristic algorithm which has been proven a successful technique. The article applies the ACO to the image edge detection, get edge image edge according to different neighborhood access policy through MATLAB simulation, and use the best neighborhood strategy to get detection. Compared with the traditional edge detection methods, the algorithm can effectively suppress the noise interference, retain most of the effective information of the image.

Reconfiguration of Shipboard Power System Using Improved Ant Colony Optimization

2011 ◽  
Vol 480-481 ◽  
pp. 1185-1190 ◽  
Author(s):  
Zheng Wang ◽  
Yong Ji Wang ◽  
Zhe Li
Keyword(s):  
Power System ◽  
Ant Colony Optimization ◽  
Nearest Neighbor ◽  
Mathematic Model ◽  
Ant Colony ◽  
K Nearest Neighbor ◽  
Improve Efficiency ◽  
System Reconfiguration ◽  
Shipboard Power System ◽  
Shipboard Power

An improved ant colony optimization is presented for solving subset problem by using k-nearest neighbor, which is used to reconfigure shipboard power system. Reconfiguration is considered as a typical subset problem, a mathematic model is built to solve it. K-nearest neighbor method is used to decrease the space of solutions and improve efficiency. The result of simulation shows that the improved algorithm can reconfigure shipboard power system efficiently.

scholarly journals Determination of Reactivity Ratios from Binary Copolymerization Using the k-Nearest Neighbor Non-Parametric Regression

Polymers ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3811
Author(s):  
Iosif Sorin Fazakas-Anca ◽  
Arina Modrea ◽  
Sorin Vlase
Keyword(s):  
Experimental Data ◽  
Nearest Neighbor ◽  
Optimization Method ◽  
Reactivity Ratios ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Integration Algorithm ◽  
Data Set ◽  
Parametric Regression ◽  
Non Parametric

This paper proposes a new method for calculating the monomer reactivity ratios for binary copolymerization based on the terminal model. The original optimization method involves a numerical integration algorithm and an optimization algorithm based on k-nearest neighbour non-parametric regression. The calculation method has been tested on simulated and experimental data sets, at low (<10%), medium (10–35%) and high conversions (>40%), yielding reactivity ratios in a good agreement with the usual methods such as intersection, Fineman–Ross, reverse Fineman–Ross, Kelen–Tüdös, extended Kelen–Tüdös and the error in variable method. The experimental data sets used in this comparative analysis are copolymerization of 2-(N-phthalimido) ethyl acrylate with 1-vinyl-2-pyrolidone for low conversion, copolymerization of isoprene with glycidyl methacrylate for medium conversion and copolymerization of N-isopropylacrylamide with N,N-dimethylacrylamide for high conversion. Also, the possibility to estimate experimental errors from a single experimental data set formed by n experimental data is shown.

Sign in / Sign up

Export Citation Format

Share Document