NutriSee: A Rule-Based Mobile Application for Personalized Dietary Planning Using Indonesian Food Composition Data - Ryan Christian Fabian Rattu; Salvius Paulus Lengkong; Salaki Reynaldo Joshua (Institute of Computer Science (IOCS), 2026)
Nutritional problems in Indonesia show a concerning trend, with adult overweight and obesity prevalences reaching 14.4% and 23.4% (2023 Indonesian Health Survey). However, existing mobile nutrition applications commonly rely on static macronutrient ratios, lack transparent decision-making mechanisms, and do not incorporate localized Indonesian food composition data, limiting their practical relevance for Indonesian users. This study aims to develop NutriSee, a mobile application for determining dietary patterns based on Body Mass Index (BMI) classification. The application was built using the Rapid Application Development (RAD) method, Flutter framework, and Firebase Firestore. The system implements BMI calculation (WHO Asia-Pacific standards) alongside Basal Metabolic Rate and Total Daily Energy Expenditure calculations utilizing the Mifflin-St Jeor equation. A rule-based system generates five daily meal recommendations based on users' caloric and macronutrient targets, using the Indonesian Food Composition Table (TKPI 2020) database. Black Box Testing with Boundary Value Analysis and Equivalence Partitioning passed all 33 scenarios. User Acceptance Testing involving 30 respondents yielded