BrewBooze: Barista and Bartender Drinks Simulator Integrated with Business Cost Prediction Using Multiple Linear Regression

This research aimed to develop an innovative system that simulates the creation of barista and bartender beverages, enhances customer satisfaction through personalized coffee and cocktail flavor recommendations, and predicts startup costs for cafés. The system uses 3D models to guide users through beverage-making processes, and the GPT OpenAI API powers its flavor recommendation engine for accuracy and personalization. Additionally, multiple linear regression was employed to provide entrepreneurs with a detailed cost breakdown based on their capital and the year of establishment. BrewBooze was evaluated through a survey using Google Forms, having IT professionals, baristas, bartenders, and general consumers as their respondents. Half of the respondents drink their coffee/cocktail more than three times a week and only a quarter of them do not know the process of making it. Results have also shown that people are satisfied with the coffee/cocktail they are ordering and for the entrepreneurial side, most respondents prefer to have a capital of less than a million pesos, making it insufficient for starting a café. The ISO 25010 software quality model assessed the system, focusing on functional suitability, reliability, performance efficiency, and usability. The results returned a very satisfactory score, indicating that BrewBooze effectively simulates beverages, provides accurate flavor recommendations, and predicts startup costs, making it a valuable tool for both consumers and aspiring café owners.