Analysis of Boarding House Feasibility and Satisfaction Using Data Mining with the C4.5 Algorithm Based on Service Quality and Facilities

This research develops a boarding house eligibility classification system using the C4.5 algorithm based on service quality and available facilities. The system evaluates boarding house eligibility by considering various factors such as management services, cleanliness, security, room facilities, public facilities, internet access, comfort, and price. Each of these factors is given a specific weight based on its importance to the tenants, and they are used to classify boarding houses as luxury, standard, and economical. The classification results show that 43% of luxury boarding houses were deemed eligible, while 57% were not. In the standard boarding house category, 21% were classified as eligible, and 79% as ineligible, while in the economical category, 23% were eligible and 77% were ineligible. Using the Confusion Matrix and Classification Report, model evaluation revealed precision ranging from 0.4 to 1.0, recall from 0.67 to 1.0, and F1-scores from 0.5 to 0.91, demonstrating a reasonably high overall accuracy. Additionally, feature importance analysis revealed that price, water and electricity availability, and room facilities are the most influential factors in determining boarding house eligibility. The system's performance was tested against a dataset of real-world boarding houses, and the results suggest that it can accurately classify boarding houses based on key factors that affect tenant satisfaction. The system has the potential to serve as a valuable decision-making tool for boarding house owners, helping them improve service quality and for prospective tenants, enabling them to make more informed housing choices based on their preferences and needs.