<![CDATA[Attention Deficit Hyperactivity Disorder is one of the common disorders that may occur on children which is indicated by certain kinds of behaviors such as the inability to calm down, not being able to pay much attention, and sudden desire to do excessive things. There are three types of ADHD in general: inattention, impulsiveness, and hyperactivity. Unfortunately, a lot of people are unaware of the dangers of this disorder if not treated from an early stage. Therefore, a system to identify the type of ADHD in a child is needed. This study implements Learning Vector Quantization as the algorithm to classify the types of ADHD and genetic algorithm as the selector of relevant features. In this study, there are 45 features which are the symptoms of ADHD that will be selected in advance by the genetic algorithm to determine which features are going to be used in the LVQ process to determine its accuracy value. The testing includes finding the numbers of variables that may have impacts to the results and can result the highest accuracy numbers. The best parameters with the highest accuracy results are the population size of 15, crossover rate of 0.9, mutation rate of 0.1, number of generations of 7, and the learning rate of 0.5 where the average accuracy is 96%.]]>
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