K-Means Algorithm for Grouping Models of Dengue Fever Prone Areas in Cirebon City

Aida Safitri; Ade Irma Purnamasari; Agus Bahtiar; Edi Tohidi

doi:10.59934/jaiea.v4i2.834

Authors

Aida Safitri STMIK IKMI Cirebon
Ade Irma Purnamasari STMIK IKMI Cirebon
Agus Bahtiar STMIK IKMI Cirebon
Edi Tohidi STMIK IKMI Cirebon

DOI:

https://doi.org/10.59934/jaiea.v4i2.834

Keywords:

K-Means; Dengue fever; Clustering; DBI; Vulnerable areas

Abstract

Dengue hemorrhagic fever (DHF) is an infectious disease transmitted through the Aedes aegypti mosquito. DHF cases in Cirebon City show a significant increase every year. This study aims to classify dengue prone areas based on case data per health center in 2020-2024 obtained from the Cirebon City Health Office. The method used is the K-Means algorithm with the Knowledge Discovery in Database (KDD) approach, which includes data selection, preprocessing, data transformation, data mining, evaluation, and knowledge. Evaluation using Davies-Bouldin Index (DBI) showed optimal results at k = 6 with a DBI value of -0.445. The clustering results produced six clusters: cluster 5 (437 dengue cases in 34 health centers) showed high risk; cluster 0 (244 cases), cluster 2 (129 cases), and cluster 3 (279 cases) showed medium risk; while cluster 1 (69 cases) and cluster 4 (86 cases) showed low risk. This study shows that the K-Means algorithm is effective in identifying DHF risk distribution patterns and provides a strategic basis for the Cirebon City Health Office to prioritize interventions and develop more effective prevention strategies.

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