Implementation of Data Mining To Determine the Association Between Body Category Factors Based on Body Mass Index
DOI:
https://doi.org/10.34288/jri.v2i4.128Keywords:
FP-Growth Algorithm, Data Mining, Associations, Body Mass Index, IMTAbstract
The development of the increasing flow of globalization in the field of science and technology as well as increased income has resulted in reduced physical activity of the community which results in diverging diet and physical activity which makes a person not pay attention to his body shape. This method of calculating the Body Mass Index can be used to determine a person's body shape. Several factors can affect the value of the Body Mass Index, including individual factors, consumption patterns, and lack of physical activity which leads to a sedentary lifestyle. These factors are made into 69 itemsets which will be used as the basis for questions in the questionnaire to collect a dataset that will later be processed using the FP Growth algorithm and looking for association rules that have the highest support x confidence value. From the 490 calculation data, the results are categorized into 10, each of which is Men with a Very Thin BMI with the highest support x confidence value of 39.56%, Men with a Thin BMI of 55.90%, Men with a Normal BMI of 70%, men with a fat BMI of 49.23%, men with an obese BMI of 41.34%, women with a very thin BMI of 41.37%, women with a thin BMI of 37.21%, Normal BMI is 68.83%, women with obese BMI are 41.65%, and women with obese BMI are 42.91%.
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