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Klasifikasi Status Gizi pada Ibu Hamil Menggunakan Metode K-Nearest Neghbor (KKN)

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This research proposes and implements a system for classifying the nutritional status of pregnant women using the K-Nearest Neighbor (KNN) method. The main objective of this research is to provide an effective solution for monitoring and identifying the nutritional status of pregnant women based on anthropometric data. The initial stage of the research involves collecting anthropometric data, including age, weight, height, gestational age, and upper arm circumference (UAC). The KNN method is applied in the training phase using training data to determine the optimal k parameter. The test results show that the classification system using the KNN method has an accuracy rate of 86.36% with a k value of 10. This confirms that the KNN approach is effective in predicting the nutritional status of pregnant women. The implications of this research include the possibility of implementing a similar system for more accurate and faster monitoring of maternal nutrition. In conclusion, the KNN method can be a reliable solution for classifying the nutritional status of pregnant women.

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Detail Information

Call Number

Informatika 612.3 Eka k

Language

Indonesia

NPM

193510389

Publisher

Pekanbaru : Universitas Islam Riau., 2024

Keyword(s)

Gizi
data mining
K-Nearest Neighbor
Nutritional Status of Pregnant Women

Other Information

Petugas

Uthi Kurnia

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