Penerapan Algoritma K-Means Clustering Untuk Pengelompokkan Penyebaran Diare Di Kabupaten Brebes

Laelatul Barokah; Fadiya Olivia Lorenza; Fitri Ayuning Tyas

doi:10.33998/processor.2025.20.1.2050

Authors

Laelatul Barokah Universitas Muhammadiyah Brebes
Fadiya Olivia Lorenza Universitas Muhammadiyah Brebes
Fitri Ayuning Tyas Universitas Muhammadiyah Brebes

DOI:

https://doi.org/10.33998/processor.2025.20.1.2050

Keywords:

Diarrhea, Brebes Regency, K-Means Algorithm, Data Mining, Davies Bouldin Index (DBI)

Abstract

Diarrhea is one of the leading causes of child mortality globally, including in Brebes Regency, Indonesia. This study aims to analyze the pattern of diarrhea spread in Brebes Regency using the K-Means algorithm based on the Knowledge Discovery in Databases (KDD) approach. The data used include the number of diarrhea cases and the area of subdistricts in 2021. The research process includes stages of data Selection, Preprocessing, Transformation, Data Mining and Evaluation of Clustering results using the Davies Bouldin Index (DBI). The analysis was conducted with RapidMiner to group regions into three clusters based on the level of diarrhea spread: high, medium and low. The results showed that the high cluster included Bantarkawung Subdistrict with 4,125 cases, while the medium and low clusters covered other subdistricts with varying case numbers. The evaluation showed a DBI value of -0.318, indicating that the clustering quality needs improvement. This study provides insights into the distribution of diarrhea in Brebes Regency, which can assist local governments in designing more effective handling strategies. Further research is recommended to improve data structure, use additional analytical methods, and consider broader data to enhance result accuracy.

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