Earticle

초록

영어

In food consumption, food addiction is behavioral and biological overlaps have been observed between eating and addictive disorders. Food addiction influence to healthy life. Food addiction caused by overeating, bingo eating, eating disorder, eating addiction, mindless eating, craving, chocaholic, and emotional eating. Determination between addiction and normal condition in food consumption, need to classification. Classification is very important in determine signs of food addiction. Classification using Naïve Bayes Algorithm and Decision Tree Algorithm. Class target is Class Normal and Class Addiction. Classification using Naïve Bayes Algorithm by criterion is Calorie Dense Food, Fatty Food, Sweet Food, Diet and Stress. This criterion as causal factor of food addiction. Classification using Decision Tree Algorithm by criterion is Stress, Fatty Food and Calorie Dense Food. This criterion as causal factor of food addiction. The experimental result is a Classification Model. This model became data source for national policy in public health.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Framework
  3.1. Classification Model
  3.2. Naïve Bayes Model
  3.3. Decision Tree Model
 4. Result and Discussion
  4.1. Classification Model
  4.2. Naïve Bayes Model
  4.3. Decision Tree Model
 5. Conclusion
 References

키워드

저자

• Adriyendi [ IAIN Batusangkar & UPI YPTK Padang, Indonesia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605