Implementasi Genetic Algorithm Dan Artificial Neural Network Untuk Deteksi Dini Jenis Attention Deficit Hyperactivity Disorder - Brillian Aristyo Rahadian; Candra Dewi; Bayu Rahayudi (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
ADHD (Attention Deficit Hyperactivity Disorder) is a psychomotor disorder that the patient is difficult to concentrate and do something excessively. Types of ADHD detection can be done by experts such as doctors, nurses and psychologists who has mastered and give solutions for therapy who affected by ADHD. However due to the limited expertise it's quite difficult to consultancy with an experts. Therefore can be made a system for early detection of ADHD. In this research, the implementation of GA-LVQ2 methods for early detection of ADHD types. Stages of implementation are population initialization, crossover, mutation, evaluation, elitism selection, and LVQ2 training. Using real coded genetic algorithm as the representation of solution. Chromosome length in this study was 45, which is a symptom of ADHD. The result of the testing has been done is the highest accuracy reached 95% in the test with 20 data testing with the parameter value of population size 10, crossover rate 0.9, mutation rate 0.1, generations 40, learning rate 0.1, the learning rate reducer 0.1, the constant value Îµ 0.35. System output is the best LVQ weights that have been tested and have high accuracy.