Klasifikasi Status Gizi Balita Menggunakan Naïve Bayes dan K-Nearest Neighbor Berbasis Web
DOI:
https://doi.org/10.30865/mib.v6i2.3566Keywords:
Toddler Nutrition, Data Mining, Classification, K-Nearest Neighbor, Naïve BayesAbstract
One of the efforts in increasing the progress of a country is improving the quality of nutrition. Good balanced nutrition can increase the body's endurance, increase the level of intelligence and make a person develop normally. Infancy is a period of development and growth that requires more nutritional content than other age groups, making it the most vulnerable to nutritional disorders. Nutritional status under 5 years can be seen using the Anthroperti method. Calculations done manually make the process take a long time and errors often occur in entering data. Data mining can make decisions on the nutritional status of toddlers faster by looking at patterns in previous data. Naïve Bayes and K-Nearest Neigbor are one of the methods in classification. Naïve Bayes has the advantage of being able to achieve high accuracy values with minimal training data. Meanwhile, the K-Nearest Neighbor method has the advantage of being able to work optimally on data that has random errors (noise). Using 412 nutritional data for toddlers, the results obtained with Naïve Bayes getting an accuracy of 80.60%, while with K-Nearest Neighbor, an accuracy of 91.79% was obtained.References
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