Implementation Of The K-Nearest Neighbor (KNN) Algorithm For Classification Of Obesity Levels
| Main Authors: | Dewi, Ayu Made Surya Indra, Dwidasmara, Ida Bagus Gede |
|---|---|
| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University
, 2020
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| Online Access: |
https://ojs.unud.ac.id/index.php/JLK/article/view/64434 https://ojs.unud.ac.id/index.php/JLK/article/view/64434/37266 |
Daftar Isi:
- Obesity or overweight is a health problem that can affect anyone. In research in several journals, it was found that obesity can be influenced by many factors, but the most dominant factors are lifestyle and diet. Obesity should not only be considered as a consequence of an unhealthy lifestyle, but obesity is a disease that can lead to other dangerous diseases. Therefore, it is important to know the level of obesity in order to take early prevention. To determine the level of obesity, a classification method is used, namely K-Nearest Neighbor (KNN) to classify the level of obesity. In this study, classification was carried out with 16 test parameters, namely Gender, Age, Height, Weight, Family History With Overweight, FAVC, FCVC, NCP, CAEC, Smoke, CH2O, SCC, FAF, TUE, CALC, Mtrans and 1 class attribute, namely Nobesity. From tests carried out using the KNN algorithm, the results obtained are 78.98% accuracy with a value of k = 2. Keywords: Obesity, KNN, Classification