Dynamic Pricing Analytic of Airbnb Amsterdam Using K-Means Clustering

The development of the world of providing accommodation services in the world is increasingly showing a very rapid development. One of these developments is in the field of information technology. One of the developments in tourism technology is the Airbnb company. Airbnb is an online-based booking or rental service for private rooms, apartments or houses. Owners can rent back accommodations on a daily basis such as hotel rooms. This research will conduct a dynamic pricing analysis that can help Airbnb hosts choose the optimal property price based on the selected season. This research uses clustering method with K-Means algorithm. The data that will be processed is Airbnb data in Amsterdam, which comes from the Airbnb dataset website for one year from 04 March 2021 to 06 March 2022. This research is expected to provide an overview of one marketing strategy by giving companies the freedom to adjust the price of service products when the situation arises. suitable and possible. Dynamic pricing is one of the marketing strategies for companies to compete with competitors. The accuracy of the research using the K-Means Clustering model is 96.21%. The net income result is 24201, the actual income is 16068, the expected profit or loss is 8133. Where this amsterdam airbnb is right to use the Dynamic Pricing method, because the income generated is optimal.