<![CDATA[The increasing number of Brawijaya Healthcare hospitals has led to the need for a more efficient and effective data management system. Data fusion is a process of combining data from multiple sources into a single data set. This can be used to improve the quality and accuracy of data, as well as to identify patterns and trends that may not be visible in individual data sets. In this paper, we propose a data fusion method for hospital data using the extract transform and load (ETL) process and the K-means clustering algorithm. The ETL process is used to clean and prepare the data for analysis. The K-means clustering algorithm is used to group the data into clusters based on their similarities. We evaluated our method using data from a real hospital in Indonesia. The results showed that our method was able to improve the quality and accuracy of the data, as well as to identify patterns and trends that were not visible in the individual data sets. The proposed method has the potential to be used in other hospitals to improve the efficiency and effectiveness of data management.]]>
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