Penerapan Algoritma Naive Bayes dan KNN pada Klasifikasi Gizi Ibu Hamil di Puskesmas Cicurug - Insany, Gina Purnama; Somantri, S; Maulina, Siti Farda (STIKOM Tunas Bangsa Pematangsiantar, 2024)
Nutrition is a very important factor for the human body, especially for pregnant women. Based on data from the 2018 Basic Health Survey (Riskades), 48.9% of pregnant women, 17.3% of whom suffer from Chronic Energy Deficiency (KEK), and 28% of pregnant women are at risk of experiencing birth complications that can cause death. Even though this figure shows a decline every year, the problem of malnutrition among pregnant women is still a major concern. In data analysis, the classification technique with the best performance was used to classify the nutritional status of pregnant women. Classification of the nutritional status of pregnant women using the supervised learning method with the Naïve Bayes and K-nearest neighbor (K-NN) algorithms. The data set used was 850 pregnant women which included the variables age, upper arm circumference (LiLA), Body Mass Index (BMI), and Hemoglobin. The research results show that the Naive Bayes algorithm has an accuracy value of 79.18% with an error value of (0.6802) with the K-NN model k=3, k=5, and k=7. Meanwhile, the K-NN k=3 and k=5 algorithms have the most optimal accuracy of 94.92% with an error value of (0.1878), while the K-NN k=7 model has an accuracy value of 93.90% with an error value of (0.2284).