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Rosmasari Rosmasari
Universitas Mulawarman
Computer Science & IT

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K-Means for Clustering of Dengue Fever Prone Areas Novianti Puspitasari; Andre Ardin Maulana; Rosmasari Rosmasari; Faza Alameka
SISFOTENIKA Vol 13, No 1 (2023): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/jst.v13i1.1337

Abstract

The number of dengue fever sufferers has increased at a reasonably high level. The high number of cases of dengue fever is sometimes different from the availability of information owned by the department or local government about the areas where dengue fever is spread. Therefore, clustering areas prone to dengue fever needs to be carried out to provide information for interested parties so that the government can take appropriate handling measures based on the level of its spread. The clustering algorithm used is K-Means. The calculation methods used are Euclidean Distance, Manhattan Distance and Minkowski Distance. Accuracy calculations of the three distance methods are carried out using the Sum of Squared Error (SSE) to determine the ideal distance calculation. In addition, SSE is also used to see the optimal number of clusters. Based on data on the number of cases of dengue fever in Samarinda City for five years from various regions, the results show that cluster C1 is a high vulnerability level, C2 is a medium vulnerability level, and C3 is a low vulnerability level. Furthermore, three clusters are the ideal number for clustering because it has a smaller SSE value. The perfect distance measurement method is the Minkowski Distance because the Minkowski Distance SSE difference is the lowest among the other distance methods, which is 13.0803.
Co-Authors Andre Ardin Maulana Faza Alameka Puspitasari, Novianti