Business Intelligence Implementation For Hotel Room Reservation Data Analysis

Abstract

The growth of the hotel industry in Indonesia is driven by increasing mobility for business and tourism purposes. However, this growth presents new challenges in hotel management, particularly in analyzing customer behavior, optimizing room availability, and making strategic decisions. Manual management of reservations and customer data is considered ineffective. This study implements Business Intelligence (BI) for analyzing hotel reservation data from 2022–2024 using OLAP and data mining techniques. BI enables the analysis of room popularity, active customer identification, payment method trends, and customer profiles. Additionally, this study applies clustering for room segmentation and forecasting methods to predict future income and reservation trends. Data is processed using ETL into a star schema-based data warehouse, visualized through Power BI dashboards. Results show that BI provides valuable insights into customer behavior, room occupancy trends, and financial performance, supporting management in improving operational efficiency and revenue.