Pemanfaatan Algoritma K-Means untuk Klastering Spasial Beban Kasus Pneumonia pada Kelompok Balita di Wilayah dengan Kepadatan Populasi Tinggi
- Rosi Windi Chrisamudra Universitas Pembangunan Jaya, Tangerang Selatan, Indonesia
-
Safrizal Abdurrahman
*
Universitas Pembangunan Jaya, Tangerang Selatan,
Indonesia
(*) Corresponding Author
Abstract
Pneumonia is one of the leading causes of death among toddlers in Indonesia, including in the city of Bandung, which records thousands of cases each year. This study aims to analyze and group cases of pneumonia in toddlers using the K-Means algorithm based on secondary data from Open Data Bandung in 2023. The research stages included data pre-processing with the main variable of Absolute Case Load, determining the optimal number of clusters using the Silhouette Method, applying K-Means with the Euclidean Distance metric, and evaluating the results using the Davies-Bouldin Index (DBI). The results show three risk clusters low (12 subdistricts), medium (12 subdistricts), and high (6 subdistricts) with Arcamanik, Bandung Kidul, Bandung Kulon, Buahbatu, Cibeunying Kidul, and Kiaracondong subdistricts identified as high-risk areas. The cluster quality evaluation produced a DBI value of 0.487, indicating fairly good cluster separation. The conclusion of this study is that clustering techniques can be used as a spatial analysis tool to support data-based health policies, which are expected to serve as a reference for local governments in optimizing resource allocation and designing more effective pneumonia prevention interventions.
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