Implementasi Metode Learning Vector Quantization (LVQ) Untuk Klasifikasi Keluarga Beresiko Stunting

Abstract

Stunting is a condition where a child's height is too short compared to children of the same age. This condition affects the health of toddlers in the short and long term, such as suboptimal body posture in adulthood, decreased reproductive health, and decreased learning capacity, resulting in suboptimal performance in school. One of the causes of stunting is a lack of nutrition, basic health facilities, and poor parenting practices. However, the current data collection and classification of families at risk of stunting still use Microsoft Excel, which is ineffective in processing large data. Therefore, the LVQ method, which is an improvement of the Vector Quantization method, is used to accelerate the classification process. In this study, 5 parameters were tested, and the optimal result was achieved by using 7 input neurons, Chebychev distance as the distance measure, a learning rate of 0.1, 7 epochs, and 30% of training data. With these parameters, an accuracy of 99.38% was obtained. Based on these results, the LVQ method can help improve accuracy in classifying families at risk of stunting