Klasifikasi Status Gizi pada Balita Menggunakan Metode Extreme Learning Machine dan Algoritme Genetika - Nabila Lubna Irbakanisa; Imam Cholissodin; Fitra Abdurrachman Bachtiar (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
Nutritional problem is one of serious problems. Because nutrition does not only concern in survival, but also relates to the quality of someone's life. In this case, the examination of child nutrient by medical personnel is generally done by archiving, namely by recording manually, and then analyzed. But by doing the analysis manually, it makes the vulnerability of inaccuracy in identifying nutritional status, and takes longer time because it is less practical. Based on these problems, the authors apply the Extreme Learning Machine (ELM) method and Genetic Algorithm to classify nutritional status in toddlers quickly and accurately. In this research, Genetic Algorithms used for finding the best input weight, which will then be used to determine the value of nutritional status using ELM. After testing, obtained an average accuracy of ELM - Genetic Algorithm is 72.3529% with the number of popsize is 100, 34 iterations, crossover rate 0.6, mutation rate 0.4, and 2 hidden neuron. While the accuracy obtained from the ELM is 67.6471%. The result also shows that the addition if Genetic Algorithm on ELM can improve the accuracy.