Nutritional Analysis Of Traditional Indonesian Food Using Machine Learning

Abstract

This research aims to analyze the nutritional composition of traditional Indonesian food using the clustering method. Nutritional data from 1,346 types of Indonesian food, including calories, protein, fat and carbohydrates, was analyzed with the help of RapidMiner Studio. The clustering results produced three main clusters: (1) foods low in nutrients, (2) foods high in nutrients, and (3) foods with moderate nutrients. Through statistical analysis, variations were found in the number of foods with certain nutritional values in each attribute. This research provides better insight into the nutritional composition of traditional Indonesian foods that are frequently consumed by the public. It is hoped that the results of this research can help individuals choose foods that suit their body's needs, as well as contribute to encouraging a healthier lifestyle in society.