Brewbooze: Barista and bartender drinks simulator integrated with business cost prediction using multiple linear regression

ABSTRACT: This research, titled “BrewBooze: Barista and Bartender Drinks Simulator Integrated with Business Cost Prediction using Multiple Linear Regression”, 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 cocts for cafes. The system utilizes 3D models to guide users through beverage-making processes, while the flavor recommendation engine is powered by the GPT OpenAI API for accuracy and personalization. Additionally, multiple linear regression is employed to provide entrepreneurs with a detailed cost breakdown based on their capital and the year of establishment. The performance of BrewBooze was evaluated through a survey of 102 respondents, including IT professionals, baristas, bartenders, and general coffee/cocktail consumers. The system was assessed using the ISO/IEC 25010 software quality model, focusing on functional suitability, reliability, performance efficiency, and usability. The results yielded an overall mean score of 3.51, with individual scores of 3.57 for functional suitability, 3.47 for reliability, 3.48 for performance efficiency, and 3.52 for usability. These findings indicate that BrewBooze effectively simulates beverages, provides accurate flavor recommendations, and predicts startup costs, making it a valuable tool for booth consumers and aspiring café owners.

Filipiniana