KLASIFIKASI STATUS GIZI BAYI POSYANDU KECAMATAN BANGUN PURBA MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM)

Abstract

This research was conducted to apply the Support Vector Machine algorithm in the process of classifying the nutritional status of infants under five. The nutritional status of early childhood can determine what kind of human resources as successors of a nation in the future. Good nutritional status plays an important role in determining the success or failure of efforts to increase human resources, so that data on the nutritional status of toddlers such as at the Posyandu, Bangun Purba District can be classified using Data Mining techniques using the Support Vector Machine algorithm. The results of this study using 80% of the data as training data and 20% of the data as training data are f1 score 0.865, accuracy 0.876, precision score 0.871, and recall score 0.876. The results showed that from a total of 347 data on the nutritional status of infants, there were 284 infants with good nutrition, 15 infants with poor nutrition, 23 infants with less nutrition, 8 infants with excess nutrition, 6 infants with obesity, and 11 infants at risk of overnutrition. Based on these results, there were 304 baby nutrition data that were classified correctly from a total of 347 baby data that were used as testing data. From this research, it can be concluded that the Support Vector Machine algorithm can classify infant nutrition data at the Posyandu, Bangun Purba District, well.