Clustering Analysis and Mapping of ISPA Disease Spread Patterns in Bireuen District

Mutammimul Ula; Tsania Asha Fadilah Daulay; Richki Hardi; Sujacka Retno; Angga Pratama; Ilham Sahputra

doi:10.29207/resti.v7i3.4936

Mutammimul Ula Universitas Malikussaleh
Tsania Asha Fadilah Daulay Universitas Malikussaleh
Richki Hardi Universitas Mulia
Sujacka Retno Universitas Malikussaleh
Angga Pratama Universitas Malikussaleh
Ilham Sahputra Universitas Malikussaleh

DOI: https://doi.org/10.29207/resti.v7i3.4936

Keywords: Clustering, ISPA, DBCAN, Mapping, Distribution Pattern Detection

Abstract

ISPA disease can be detected by analyzing the regional distribution map of the disease. Early detection of ARI is very important for effective prevention. The study conducted in Bireuen Regency used data from 2019 to 2021, sourced from dr. Fauziah Bireuen Hospital, revealed that there was an increase in ARI cases from an average of 13.18 to 59.24 per year. The aims of the study were to identify ARI clusters, analyze disease patterns using Spatial Pattern Analysis and Flexible Shaped Spatial Scanning Statistics. The methodology involves collecting patient data for each ARI case and processing it using DBSCAN to obtain cluster points on the map. Spatial Pattern Analysis is used to analyze these clusters and identify hotspot points on the map. The analysis resulted in four clusters: Cluster 1 (6 subdistrict), Cluster 2 (4 subdistrict), Cluster 3 (1 subdistrict), and Cluster 4 (6 subdistrict). The study identified 6 hotspots in 2019, 5 hotspots in 2020, and 6 hotspots in 2021. Each ARI disease clustering map shows the distribution of ARI cases and identifies areas prone to the disease. These findings provide valuable insights for targeted interventions and preventive actions in identified high-risk areas of ISPA.

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