A Web-Based Personalized Diet Recommendation System Using Decision Tree for Food Suitability Classification

Abstract

Obesity and unhealthy eating patterns have become significant health concerns due to poor dietary habits and a lack of personalized nutritional guidance. Existing food recommendation systems often provide general recommendations without considering individual calorie and nutritional requirements. Therefore, this study aims to develop a web-based diet food recommendation system that integrates nutritional requirement calculations and Decision Tree-based food suitability classification. The system utilizes user information, including age, gender, weight, height, physical activity level, and diet goals, to calculate nutritional requirements through Body Mass Index (BMI), Basal Metabolic Rate (BMR) using the Mifflin-St Jeor method, and Total Daily Energy Expenditure (TDEE). A food dataset containing Indonesian foods and beverages was preprocessed and labeled using a rule-based approach based on macronutrient similarity scores. The Decision Tree algorithm was implemented to classify foods into suitable and unsuitable categories according to users’ nutritional requirements. Suitable foods were subsequently processed through a scoring mechanism and meal construction procedure to generate personalized meal plans. Experimental results showed that the Decision Tree model achieved an accuracy of 92.50%, precision of 78.26%, recall of 94.74%, and F1-score of 85.71%. System testing demonstrated that the developed features functioned properly and generated structured diet recommendations automatically. In conclusion, the proposed system can assist users in selecting foods according to their nutritional requirements and support healthier dietary planning.