<![CDATA[This development research was carried out to create a boarding house price prediction information system product that could help the problems of entrepreneurs and boarding house users regarding the determination of boarding prices. This development research was carried out to create a boarding house price prediction information system product that could help the problems of entrepreneurs and boarding house users regarding the determination of boarding prices. The research method used in this development research process is Research and Development with the Waterfall Model method. During the development process, tests were also carried out to determine the quality of the information system developed by referring to the ISO 25010:2011. The results of this research and development process are (1) Linear Regression algorithm as the best Machine Learning model that can make relevant and accurate predictions of boarding house rental prices. (2) A web-based boarding price prediction information system in the Yogyakarta Region was developed using the Waterfall development model and the Flask framework, which can automatically predict the relevant prices of the facilities selected by the user. (3) Test results on the Functional Suitability aspect ensure that all functions in the information system can run properly. The usability aspect produces 86.7% (Very Good category). The Performance Efficiency aspect produces a percentage of 100% (Very Good Performance), and fully loaded web page time is only 0.707 seconds (Good category). The reliability aspect results in a percentage of system resilience of 100% on sessions, pages, and hits in testing.]]>
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