Daily Nutritional Planning System Based on Portion Optimization and K-Means Clustering

Abstract

Abstract — Personalized daily nutritional planning is a complex challenge due to the difficulty of translating individual nutritional needs into accurate food portions, exacerbated by the high prevalence of dual nutritional burdens in Indonesia. This study aims to design and implement an intelligent daily nutrition Decision Support System (DSS) capable of generating measured menu recommendations. The research method employs a hybrid approach, integrating an expert system knowledge base (Mifflin-St Jeor, FAO, IOM rules) with an inference engine based on dynamic portion optimization using linear programming (PuLP). Furthermore, unsupervised machine learning (K-Means) is applied to cluster food items to generate educational nutritional labels. The system was implemented as a web application using Python Flask and tested through case studies and functional verification. The main finding shows that the optimization engine successfully generated daily meal plans with specific grammages that closely approximated the target calories and macronutrients (case study caloric deviation <3%). The K-Means integration also proved effective in providing functional labels (e.g., "Pure Protein", "Energy Dense") for food items. This study concludes that a hybrid architecture based on dynamic portion optimization can provide a diet planning tool that is more quantitatively accurate and informative than traditional qualitative approaches.