IMPLEMENTASI LOGIKA FUZZY MAMDANI DALAM SISTEM PENILAIAN KESEHATAN MAKANAN KEMASAN BERDASARKAN LABEL NUTRITION FACTS

The growth of the packaged food industry has increased the need for an easy-to-understand health assessment system for consumers, especially those with limited nutrition literacy. This study develops a Mamdani fuzzy logic-based decision support system to evaluate the healthiness of packaged foods using Nutrition Facts labels. The system processes nutritional parameters such as fat, sugar, salt, fiber, protein, fruit/vegetable/nut content, and calorie content, converting them into linguistic categories like "low," "moderate," and "high" for easier interpretation by lay users. It effectively handles uncertainties and ambiguities in nutrition data, providing classifications like "Unhealthy," "Healthy," or "Very Healthy." Implemented through a web platform using Python and Flask, the system was tested with five food samples, achieving an 80% agreement with the official NutriScore classification. This indicates the potential of the system as a reliable, practical tool to help consumers make quicker and more accurate dietary decisions and improve nutrition awareness.