Optimasi K-Nearest Neighbour Menggunakan Particle Swarm Optimization pada Sistem Pakar untuk Monitoring Pengendalian Hama pada Tanaman Jeruk - Kukuh Wiliam Mahardika; Yuita Arum Sari; Achmad Arwan (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
Orange in Indonesia is one of the national commodities have the potential of high competitiveness in the local economy to abroad. The production of Indonesian Orange from 2006 to 2015 has decreased. One of the causes of this decline is pests. Therefore we need a system that can identify pests in citrus plants. The PSO-KNN method is one method that can be used to solve classification problems with many features. This method is a combination of 2 methods namely K-Nearest Neighbour and Particle Swarm optimization. K-Nearest Neighbors (KNN) are used to classify pests based on similarity calculations from existing data. Particle swarm optimization (PSO) is used to perform k value optimization and feature selection on KNN dataset and then evaluate the accuracy generated on KNN. From the results of tests that have been done can be concluded that the value of the best PSO parameter iteration is 151, popsize is 25, the value of c1 is 1, the value of c2 is 1.2 and w is 0.9. There was an increase in accuracy before and after optimization that is the highest accuracy of KNN reaches 90%, and the highest accuracy of PSO-KNN reached 96.25%. Improved accuracy indicates that the PSO algorithm is able to correct the deficiency that exist in KNN.