Penerapan Normalisasi Data Metode Decimal Scaling Dan Metode K-Means Dalam Mengelompokkan Kasus Demam Berdarah
DOI:
https://doi.org/10.30865/klik.v4i6.1925Keywords:
Dengue Fever; Data Mining; Data Normalization; Decimal Scaling; K-MeansAbstract
Dengue fever, caused by a virus transmitted through the bite of Aedes aegypti and Aedes albopictus mosquitoes, continues to show an alarming trend of increasing cases. Although prevention and control efforts have been widely implemented, this increase is raising serious concerns among public health experts and governments. The causes of the increase in dengue fever cases in 2024 may vary, including climate change which affects the distribution of mosquitoes that carry the virus, urbanization which increases mosquito habitat, and changes in human behavior that affect the level of environmental cleanliness. However, this data is often scattered and has different scales, making it difficult to directly analyze. Data analysis of dengue fever cases is important to understand the pattern of disease spread and take preventive steps using the Decimal Scaling method and grouping data using the K-Means method helps in understanding patterns of dengue fever cases. Where is the Decimal Scaling method to produce better and balanced data. After the data is normalized, the next process is to explore information on dengue fever data by applying data mining grouping using the K-Means method. Based on the results of the cendroit test results where cluster 0 has a greater value for each dengue fever grade value as seen in Figure 4, the test results were obtained with a total sample of 197 test data from 2019 to 2023 with 2 number of clusters where cluster 0 has 81 members and cluster 1 has 115 members. So it can be concluded that those who get priority for treatment are more important in the C0 cluster group.
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