In general, obesity is calculated using the BMI, which is found to have limitations of use when we insert only one type of non-quantitative data as a crisp value, which is commonly used, or even create an obesity forecasting model using data mining techniques. If there is an input of weight and height data as a forecasting factor in various types of data, such as crisp value, estimated value and Fuzzy Linguistic Term value, etc. The algorithm will not possibly work under the data mining technique with such input data.
This research presented the solution for this problem in order to enable data mining techniques under limitations of inserted data, including crisp value, estimated value and fuzzy linguistic term value, for forecasting obesity. The researchers created a fuzzy database system in the form of Conceptual Meta Schema to support fuzzy attributed data storage. In addition, there were 3 types of fuzzy attribute matching as follows; Type 1 was matching crisp value with fuzzy linguistic term value, Type 2 was matching estimated value with fuzzy linguistic term value, and Type 3 was matching fuzzy linguistic term value with fuzzy linguistic term value, respectively. The attribute node from these fuzzy attributes would be selected to be used in the work of data mining techniques. Therefore, in this research, the researchers presented and compared the fuzzy model of working under the algorithm of data mining techniques in the form called fuzzy neural network and fuzzy decision tree techniques. The research result, it was found that the appropriate models for predicting obesity were fuzzy neural network algorithm with the neural network structure being 31-3-3, momentum at 0.2, learning rate at 0.3, under dividing data to test with cross-validation folds = 10 yielded accuracy value, precision value, recall value and f-measure at 84.3%, 82.7%, 84.3%, and 82.8%, respectively.
Keywords: Obesity, Fuzzy Neural Net, Fuzzy Decision Tree, Fuzzy Attribute Matching, Forecasting Model
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