Decision Support Systems to Selection of Diet Type Using Fuzzy Sugeno and Naïve Bayes Method

  • Youllia Indrawaty Nurhasanah Department of Informatics, Faculty of Industrial Technology, Institut Teknologi Nasional (Itenas) http://orcid.org/0000-0002-6497-7932
  • Asep Nana Hermana Department of Informatics, Faculty of Industrial Technology, Institut Teknologi Nasional (Itenas)
  • Mahesa Arga Hutama Department of Informatics, Faculty of Industrial Technology, Institut Teknologi Nasional (Itenas)

Abstract

Sugeno Fuzzy algorithm is one of the algorithms contained on Fuzzy Inference System, that used to describe the condition between the two pieces of the decisions represented in the form of rules IF - THEN, where the output is constant or linear equations. While the Naive Bayes algorithm is an algorithm that uses data classification to a particular class based on the probability of each data class. Both of these algorithms can be implemented on a Decision Support System (DSS) for diet selection, using Fuzzy Sugeno as an additional determinant of energy and Naive Bayes method as decision maker. This is because the need for food intake and diet has become a problem for humans. To prevent excess intake of food it needs dietary adjustments or so-called diet. But in daily life, people sometimes hard to determine the type of diet that is suitable for them. So we need a system that can determine the type of diet that is suitable for a person. The data that used as a reference for decision support are age, daily caloric requirement, Body Mass Index (BMI), blood pressure, cholesterol, uric acid and blood sugar levels. Results of system testing showed from a sample of 30 data there are 26 appropriate data and 4 inappropriate data to determine the type of diet by the system with the success rate of 86.7%.

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Published
2017-12-07
How to Cite
NURHASANAH, Youllia Indrawaty; NANA HERMANA, Asep; ARGA HUTAMA, Mahesa. Decision Support Systems to Selection of Diet Type Using Fuzzy Sugeno and Naïve Bayes Method. IJAIT (International Journal of Applied Information Technology), [S.l.], v. 1, n. 02, p. 115-127, dec. 2017. ISSN 2581-1223. Available at: <http://journals.telkomuniversity.ac.id/ijait/article/view/894>. Date accessed: 15 aug. 2018. doi: https://doi.org/10.25124/ijait.v1i02.894.
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Articles