Klasifikasi Status Stunting Balita Dengan Metode Support Vector Machine Berbasis Web

Abstrack - Parents should pay attention to their children during their toddler years because at that age, they are vulnerable to various growth and developmental disorders, one of which is stunting. Stunting is a growth and developmental disorder caused by nutritional deficiencies and is characterized by a height that does not meet the normal growth criteria for children of the same age. To prevent stunting, healthcare workers or integrated health post (Posyandu) cadres measures the anthropometry of children’s bodies at Posyandu. The data from these body measurements are then processed manually, which poses a significant risk of processing errors due to human error. By studying the patterns in measurement data, data mining can help address issues in the data processing process. Support Vector Machine (SVM) is one of the commonly used data mining methods for classification problems, known for its ability to work with small memory and separate data that cannot be linearly separated. Age, gender, Early Initiation of Breastfeeding (EIBF), weight, and height are the attributes used for classification using the SVM algorithm. Based on the conducted tests, there were 1172 data points with an average performance result of the best model using the parameter γ = 0.01, achieving an accuracy of 98.99%. This means that the model can be used to accurately predict new measurement data, enabling timely preventive measures for stunting.