Optimasi Vektor Bobot Pada Learning Vector Quantization Menggunakan Particle Swarm Optimization Untuk Klasifikasi Jenis Attention Deficit Hyperactivity Disorder (ADHD) Pada Anak Usia Dini - Windi Artha Setyowati; Wayan Firdaus Mahmudy (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Attention Deficit Hyperactivity Disorder (ADHD) is a mental development disorder that has a major characteristic of inability to focus attention and hyperactivity. Behavior that characterizes ADHD often appears in children aged 3-5 years. ADHD consists of 3 types, namely: inattention, hyperactivity, and impulsivity. Not many people are aware of ADHD, then needed a system for the classification type of ADHD. By observing visible symptoms, ADHD can be classified using the Learning Vector Quantiztion (LVQ) algorithm, but the LVQ algorithm produces a fairly low accuracy. To optimize the accuracy level of LVQ algorithm, Particle Swarm Optimization (PSO) algorithm is used. The PSO is used to find the best LVQ weight vector. To know the difference of accuracy result, 2 test is done, that is LVQ-PSO and LVQ test. The test uses the same data. Test results showed that LVQ-PSO algorithm yielded highest accuracy 87,3% in 84,6 seconds, while LVQ algorithm yielded highest average accuracy of 80,6% in 4,8 seconds. The best parameters of PSO yielding the best accuracy are Wmax 0,6, Wmin 0.5, swarm size 100, maximum iteration PSO 100, Î± 0,1, and dec Î± 0,1. From the results of the test accuracy it can be concluded that the PSO algoritme can be used to optimize the LVQ algorithm even though it takes longer computation time.