The increasing competition among fast food restaurants in urban areas has made consumers more selective in their purchasing decisions, with price and product quality emerging as key determinants. This study aims to analyze the influence of perceived price and perceived product quality on consumer purchasing decisions at Subway fast food restaurants in East Bekasi. A quantitative approach was applied using a structured questionnaire distributed to 50 consumers. Data were analyzed through multiple linear regression and classical assumption testing. The results indicate that price has a significant and positive effect on purchasing decisions, with a regression coefficient of 1.191 and a partial determination coefficient (R²) of 0.750, suggesting that price alone explains 75% of the decision variance. Similarly, perceived product quality also demonstrates a significant positive effect with a coefficient of 0.469 and R² of 0.724. When combined, both variables explain 72.4% of the variance in purchasing decisions, with the F-test result of 91.673 surpassing the critical value of 3.20. These findings suggest that consumers in East Bekasi prioritize both affordability and quality when choosing fast food. This research contributes to the literature by validating the dual importance of economic and experiential value in fast food marketing strategies, especially in highly competitive suburban environments.

The prevalence of Ear, Nose, and Throat (ENT) diseases presents diagnostic challenges, especially in resource-limited settings. At THB Bekasi Hospital, constrained specialist availability and long consultation queues highlight the need for an accessible diagnostic solution. This study aims to develop an expert system for diagnosing ENT diseases using the Extreme Programming (XP) methodology, incorporating the forward chaining technique for inference. The research includes assessment, knowledge acquisition through expert consultations, system design, and rigorous testing. The system was developed as a mobile application using Android Studio, enabling users to input symptoms and receive real-time diagnostic insights. The knowledge base integrates data from medical experts, synthesizing 11 diseases and 35 symptoms into a robust decision-making framework. The diagnostic process applies predefined rules to ensure accuracy in identifying conditions such as sinusitis, laryngitis, and otitis. Evaluation results demonstrate a 100% match accuracy during testing with 15 test cases, confirming the system’s reliability. The application offers users rapid diagnostic assistance, promoting timely treatment for ENT issues, although it does not substitute medical professionals. By leveraging ubiquitous smartphone access, this system addresses gaps in healthcare accessibility and enhances patient autonomy. This research contributes a scalable framework for deploying expert systems in other medical domains. Future improvements include integrating geolocation services for nearby specialist referrals and adopting backward chaining for more complex diagnoses, thereby broadening its applicability and utility