COMPARISON OF DATA MINING ALGORITHM FOR CLUSTERING PATIENT DATA HUMAN INFECTIOUS DISEASES - Nurfadilla, Nadia; Afdal, M.; Permana, Inggih; Zarnelly, Zarnelly (Informatika, Universitas Jenderal Soedirman, 2023)
Tuberculosis is known as an infectious disease whose transmission through air intermediaries is caused by the germ Mycobacterium Tuberculosis. This disease has become a case that has almost spread throughout the pelalawan Regency with the number continuing to increase every year so that it is possible to be able to group the areas where this disease spreads. Grouping of tuberculosis data distribution areas using data mining methods in the form of clustering with the data used coming from the Pelalawan Regency Health Office from 2020 to 2022. The data obtained earlier will then be processed using k-medoids, k-means, and x-means algorithms. The beginning of this research was by processing data from each year using these three algorithms. Determination of the most optimal algorithm using DBI or known as the Davies Bouldin Index. The results of the processing of existing indicators are grouped into three sections, namely areas with a high, medium, and low number of cases. From the results of the study, the optimal algorithm in 2020 data is the k-medoids algorithms with a DBI value of 0,553 and in 2021 data, the most optimal algorithm is the k-means and x-means algorithm with similar DBI values of 0,582. Furthermore, the data in 2022 the most optimal algorithms are the k-means and x-means algorithms because they have the same DBI value, which is 0,510.