Implementation of Principal Component Analysis and Learning Vector Quantization for Classification of Food Nutrition Status

Abstract

Balanced nutrition is very good in the process of child development. During the COVID-19 pandemic, consuming a balanced nutritious diet can keep a child's immune system from transmitting the virus. In determining the nutritional content of children's food during the pandemic, a classification of the nutritional content of children's food is carried out by applying the principal component analysis (PCA) dimension reduction method and the learning vector quantization (LVQ) classification method. The data used in this study amounted to 1168 data with 25 indicators of food nutrients. From the tests that have been carried out, the combination of the PCA-LVQ method produces an average accuracy of 58% with the highest accuracy of 60%. In addition, this study also compares the performance of the PCA dimension reduction method, independent component analysis (ICA) and factor analysis (FA) on the LVQ classification process. The final result of testing the three methods is that the FA method takes the fastest time, which is 4.10434 seconds and the PCA method produces the highest accuracy, which is 58.2%

Keywords

• food nutrition, covid-19 pandemic, classification, principal component analysis, learning vector quantization