<![CDATA[Daily calorie needs play an important role in maintaining health and supporting a balanced lifestyle. Conventional approaches, such as Basal Metabolic Rate (BMR) and Total Daily Energy Expenditure (TDEE), are commonly used to estimate calorie requirements. However, these methods may have limitations in representing individual physiological variability. This study developed a web-based daily calorie recommendation system using a Hybrid Mamdani Fuzzy approach. The proposed method integrates conventional BMR and TDEE calculations with Mamdani fuzzy inference to generate individualized calorie recommendations. The input variables included age, height, weight, gender, and physical activity level. The fuzzy inference process consisted of fuzzification, rule evaluation using 81 fuzzy rules, aggregation with the MAX operator, and centroid defuzzification. The system was implemented using Python, Flask, and the Scikit-Fuzzy library. Statistical evaluation was conducted using 100 testing scenarios representing various user characteristics. The results showed that the proposed system generated calorie adjustment values ranging from approximately −95 kcal/day to +89 kcal/day relative to conventional TDEE calculations. These findings indicate that the fuzzy inference mechanism functioned as a refinement layer within the calorie recommendation process. Overall, the proposed system demonstrates the feasibility of integrating conventional calorie estimation methods with fuzzy reasoning in a web-based decision-support environment for nutritional planning.]]>
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