Classification of Foods Based on Nutritional Content Using K-Means and DBSCAN Clustering Methods
DOI:
https://doi.org/10.34148/teknika.v13i3.1067Keywords:
Clustering, K-Means, DBSCAN, NutritionalAbstract
This study classifies foods based on their nutritional content using K-Means and DBSCAN clustering methods. The clustering quality was evaluated using the Davies-Bouldin Index (DBI) and Silhouette Score. K-Means was tested with different k values, while DBSCAN was analyzed with varying min_samples parameters. Additionally, a function was developed to group foods into three categories: Weight Gain, Obesity Prevention, and Weight Loss, based on calories, protein, fat, and carbohydrate content. The results show that K-Means is more effective than DBSCAN in clustering foods by nutritional content, yielding lower DBI values and higher Silhouette Scores. For example, K-Means with k = 3 achieved a DBI of 0.694930 and a Silhouette Score of 0.538921, while DBSCAN with eps = 0.75 and min_samples = 4 produced a DBI of 0.34546577 and a Silhouette Score of 0.492830814. This study concludes that K-Means provides superior clustering performance, enabling more specific dietary recommendations tailored to individual nutritional needs.
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