Design Of A Decision Support System For Food Menu Recommendations For Gerd Sufferers Using The Topsis Method

People with Gastroesophageal Reflux Disease (GERD) need to choose food carefully because nutrient content, acidity, gas-producing potential, and caffeine may influence symptoms. Manual selection is hard to perform consistently because it involves many alternatives and criteria. This study designs a web-based decision support system that recommends food menus for GERD patients. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was chosen because it ranks alternatives under multiple benefit and cost criteria with different weights, producing a 0–1 preference value that can be verified manually. The system was developed with the Waterfall model, designed using UML, and implemented with Laravel and MySQL. The database contains 90 menu alternatives (30 foods, 30 desserts, 30 beverages) scored on eight criteria using a 1–5 scale. The highest preference values were obtained by Nasi Merah + Ayam Goreng (0.6526), Bread Pudding (0.7679), and Jus Banana (0.7970) for foods, desserts, and beverages, whereas the lowest were Nasi Pecel (0.3436; acidity score 4 and gas score 5), Chocolate Ice Cream (0.3619), and Kopi Susu Palm Sugar (0.3744; caffeine score 4). Black box testing of eight functional scenarios yielded eight valid scenarios (100%). 10 GERD patients using simulated data showed a mean symptom score decrease from 5.4 to 3.0 (0–10 scale), 9 of 10 patients (90%) were successful, and no severe symptoms occurred. The system is a decision aid and does not replace consultation with healthcare professionals.