Identifikasi Klaster Wilayah Rawan Demam Berdarah Dengue Dengan Pendekatan Spatial ‘K’luster Analysis by Tree Edge Removal (SKATER) di Kabupaten Lamongan

Melati, Cahya Eka (2026) Identifikasi Klaster Wilayah Rawan Demam Berdarah Dengue Dengan Pendekatan Spatial ‘K’luster Analysis by Tree Edge Removal (SKATER) di Kabupaten Lamongan. Undergraduate thesis, UPN Veteran Jawa Timur.

Preview

Text (Cover)
22083010090-cover.pdf
Download (1MB) | Preview

Preview

Text (BAB 1)
22083010090-bab 1.pdf
Download (270kB) | Preview

Text (BAB 2)
22083010090-bab 2.pdf
Restricted to Repository staff only until 8 July 2029.
Download (688kB)

Text (BAB 3)
22083010090-bab 3.pdf
Restricted to Repository staff only until 8 July 2029.
Download (900kB)

Text (BAB 4)
22083010090-bab4.pdf
Restricted to Repository staff only until 8 July 2029.
Download (2MB)

Preview

Text (BAB 5)
22083010090-bab5.pdf
Download (121kB) | Preview

Preview

Text (Daftar Pustaka)
22083010090-daftar pustaka.pdf
Download (150kB) | Preview

Text (Lampiran)
22083010090-lampiran.pdf
Restricted to Repository staff only until 8 July 2029.
Download (349kB)

Abstract

Dengue Hemorrhagic Fever (DHF) remains a public health problem in Indonesia, with the number of cases continuing to increase each year. Lamongan Regency is one of the regions in East Java Province experiencing an increase in DHF cases with uneven distribution across sub-districts. This condition highlights the importance of spatial analysis to identify disease distribution patterns and determine priority areas for intervention. This study aims to identify clusters of DHF-prone areas in Lamongan Regency using the Spatial ‘K’luster Analysis by Tree Edge Removal (SKATER) method. The research data include Dengue Incidence Rate (IR), population density, area size, percentage of proper sanitation, percentage of household waste management (PSRT), and rainfall across 27 sub-districts in Lamongan Regency. Moran’s I analysis showed that most variables had significant spatial autocorrelation. The optimal number of clusters was determined using Particle Swarm Optimization (PSO) and Adaptive Particle Swarm Optimization (APSO). The results showed that the optimal number of clusters was five, with a Sum of Squared Error (SSE) value of 84.1297. The resulting clusters were categorized into very low, low, moderate, high, and very high vulnerability levels. Ngimbang Sub-district was classified as very low vulnerability, while Karangbinangun Sub-district was classified as very high vulnerability. The research results were also implemented in a Graphical User Interface (GUI)-based application to facilitate visualization and interpretation of the clustering results. This study demonstrates that the SKATER method optimized using PSO and APSO can produce spatially representative clustering of DHF-prone areas, which can be used as a basis for determining intervention priorities and supporting decision-making in DHF control policies in Lamongan Regency.

Item Type:

Thesis (Undergraduate)

Contributors: