Classification Using Naïve Bayes and Decision Tree on Food Addiction

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 Naive Bayes Algorithm and Decision Tree Algorithm. Class target is Class Normal and Class Addiction. Classification using Naive 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.