Penerapan Algoritma Naïve Bayes Classifier Dalam Klasifikasi Status Gizi Balita dengan Pengujian K-Fold Cross Validation
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Nurainun Nurainun
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
- Elin Haerani Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Fadhilah Syafria Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Lola Oktavia Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
Nutritional status is a condition related to nutrition that can be measured and is the result of a balance between nutritional needs in the body and nutritional intake from food. In Indonesia, there are still many nutritional problems such as malnutrition and other nutritional problems. This research will use the Naïve Bayes Classifier algorithm with K-Fold Cross Validation testing. The data used is data on the nutritional status of toddlers in August 2022 at the Rambah Samo I Health Center. Attributes in this study include Gender, Birth Weight, Birth Height, Age at Measurement, Weight, Height, ZS BB/U, BB/U, ZS TB/U, and TB/U. Determination of the nutritional status of toddlers in this study was based on the BB/TB index which consisted of 6 classes, namely severely wasted, wasted, normal, possible risk of overweight, overweight, and obese. From the research conducted, it was found that the Naïve Bayes Classifier algorithm with K-Fold Cross Validation can correctly classify the nutritional status of toddlers. From data processing using 10-Fold Cross Validation on the Naïve Bayes Classifier algorithm, it is known that the highest accuracy value is 82.94% in the 5th iteration, while the lowest accuracy value is 65.88% in 6th iteration. With an average overall accuracy value of 75.47%. Meanwhile, the average precision value obtained is 81.36% and the average recall value is 75.47%.
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