IMPLEMENTASI ALGORITMA K-MEANS CLUSTERING PADA SISTEM INFORMASI PERSEBARAN PENYAKIT DEMAM BERDARAH BERBASIS WEBGIS
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Institusion
Universitas Muhammadiyah Ponorogo
Author
WAHYUDI, IRKHAM TRI
Subject
T Technology (General)
Datestamp
2023-09-07 08:43:45
Abstract :
Dengue Hemorrhagic Fever (DHF) is a contagious zoonotic disease that has a significant impact on public health. Dengue virus is transmitted by Aedes aegypti and Aedes albopictus mosquitoes, with symptoms such as fever, nausea, headache and abdominal pain. The 3M program (Drain, Close, Grave) and counseling are used by the Magetan District Health Office to reduce the spread of DHF. However, the lack of actual data regarding high-risk areas is an obstacle. The solution is to create a reporting system and data grouping using the k-means clustering algorithm. DHF patient data are grouped into three clusters representing low, medium and high risk. This information is presented in a color-coded web-GIS-based map for each cluster. The aim is to provide an interactive and informative visualization to the Health Service and the public. This study aims to build a system that can manage DHF data reporting and produce prevalence maps based on k-means clusters. This map is expected to assist the Health Office in more effective preventive actions and provide information on the location of DHF risks to the public. This system has the potential to improve efforts to prevent the spread of DHF and better manage public health.
Dengue Hemorrhagic Fever (DHF) is a contagious zoonotic disease that has a significant impact on public health. Dengue virus is transmitted by Aedes aegypti and Aedes albopictus mosquitoes, with symptoms such as fever, nausea, headache and abdominal pain. The 3M program (Drain, Close, Grave) and counseling are used by the Magetan District Health Office to reduce the spread of DHF. However, the lack of actual data regarding high-risk areas is an obstacle. The solution is to create a reporting system and data grouping using the k-means clustering algorithm. DHF patient data are grouped into three clusters representing low, medium and high risk. This information is presented in a color-coded web-GIS-based map for each cluster. The aim is to provide an interactive and informative visualization to the Health Service and the public. This study aims to build a system that can manage DHF data reporting and produce prevalence maps based on k-means clusters. This map is expected to assist the Health Office in more effective preventive actions and provide information on the location of DHF risks to the public. This system has the potential to improve efforts to prevent the spread of DHF and better manage public health.
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