ANALISIS PREDIKSI KONSUMSI GIZI PADA PROGRAM MAKAN BERGIZI GRATIS (MBG) MENGGUNAKAN METODE MACHINE LEARNING - Tirta Bening Sedayu; Nia Saputri; Tia Putri Akhmaliyah; Muhammad Hanif; Dicky Apdillah (Smart Education, 2026)
The Free Nutritional Meal Program (MBG) is a government effort to improve the nutritional quality of students by providing nutritious food. This study aims to analyze the prediction of nutrient consumption levels in the MBG Program using the K-Nearest Neighbor (KNN) method. The data used were 100 datasets consisting of variables such as age, menu type, menu calories, initial nutritional status, and nutrient consumption levels. The data were divided into 80% training data and 20% testing data with a K value of 5. The test results showed that the KNN method achieved 95% accuracy, with 19 data points correctly predicted out of 20 test data points. The results indicate that the KNN method is capable of predicting nutrient consumption levels effectively and can be an alternative to support the evaluation of the Free Nutritional Meal Program (MBG).