Analysis and Comparison of the Performance of the K-Means Algorithm and the X-Means Algorithm in Clustering Disease Types in Mitra Medika Hospital - Herdawani Afdilla (Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal, 2024)
The system currently used by the hospital is still manual in managing patient data and information. What happens at Mitra Medika Hospital is that it is difficult to provide medical needs related to the diseases experienced by patients, considering that there are many types of diseases, so they provide many medical needs. Several inpatients have used BPJS facilities for various diseases suffered by the patient to carry out further examinations so that they can recover from the disease they are suffering from. Mitra Medika Hospital only looks at medical needs based on the disease suffered by the patient, however, seeing the large amount of patient history data makes it very difficult for Mitra Medika Hospital to find out the groups of diseases that patients often experience. This research uses a quantitative approach which starts from a theoretical framework, expert ideas, and researchers' understanding based on their experience, then developed into problems and solutions that are proposed to obtain justification (verification) or assessment in the form of empirical data support in the field. Here we apply a data mining pattern where data mining is extracting very large data (big data). Cluster 0: Of 245 Men (Suffering from 1-5 Diseases) Cluster 1: Of 255 Women (Suffering from 6-10 Diseases) Using the K-Means Algorithm and X-Means Algorithm can produce clustering. By using disease history data, you can apply the K-Means Algorithm and X-Means Algorithm methods to determine clusters. By using web programming, we can produce an analysis and comparison of the performance of the K-Means algorithm and the X-Means algorithm in clustering disease types in hospitals. Medika Partners.