Pemanfaatan Data Rekam Medis Kasus Penyakit TBC Secara Spasial di Wilayah Kecamatan Wonosari

This research is motivated by the large number of Tuberculosis (TB) that is spreading and rampant in the community, including in Wonosari sub-district. The purpose of this study is to analyze the spread of TB cases in Wonosari District using a spatial approach. This study used medical record data from tuberculosis patients treated at the Wonosari Public Health Center over a certain period. The analysis methods used were Average Nearest Neighbor (ANN) to evaluate the spatial distribution pattern of TB cases, and Moran's Index to identify the presence of spatial autocorrelations in the spread of cases. The results of the analysis showed that TB cases in Wonosari City had an ANN of less than 1, indicating that TB cases were clustered patterns. Based on the results of Moran's I Global analysis for TB cases, a p-value greater than 0.05 indicates that this distribution pattern occurs by chance and does not reflect a real spatial correlation. The ANN of health care facilities in Wonosari City is above 1 and a positive z-score which indicates that health facilities tend to be more dispersed. The conclusion of this analysis provides an important picture of the distribution of diseases and health facilities in Wonosari City, which can be the basis for more effective public health intervention planning. With the use of spatial medical record data, it is hoped that efforts to control tuberculosis in Wonosari District can be improved. The implications of this research are expected to be one of the solutions for local governments and public knowledge about understanding the spread of TB cases. This research also opens up opportunities for further studies, especially examining the factors and causes of tuberculosis in this region.

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Publication Details

Journal
MASALIQ
Published
2025-04-28
DOI
https://doi.org/10.58578/masaliq.v5i3.5522
Primary Topic
Data Mining and Machine Learning Applications
Type
article
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article

Pemanfaatan Data Rekam Medis Kasus Penyakit TBC Secara Spasial di Wilayah Kecamatan Wonosari

Andhy Sulistyo, Resmiaini Resmiaini
MASALIQ
Data Mining and Machine Learning Applications
article

Pemanfaatan Data Rekam Medis Kasus Penyakit TBC Secara Spasial di Wilayah Kecamatan Wonosari

Andhy Sulistyo, Resmiaini Resmiaini
article en

Abstract

This research is motivated by the large number of Tuberculosis (TB) that is spreading and rampant in the community, including in Wonosari sub-district. The purpose of this study is to analyze the spread of TB cases in Wonosari District using a spatial approach. This study used medical record data from tuberculosis patients treated at the Wonosari Public Health Center over a certain period. The analysis methods used were Average Nearest Neighbor (ANN) to evaluate the spatial distribution pattern of TB cases, and Moran's Index to identify the presence of spatial autocorrelations in the spread of cases. The results of the analysis showed that TB cases in Wonosari City had an ANN of less than 1, indicating that TB cases were clustered patterns. Based on the results of Moran's I Global analysis for TB cases, a p-value greater than 0.05 indicates that this distribution pattern occurs by chance and does not reflect a real spatial correlation. The ANN of health care facilities in Wonosari City is above 1 and a positive z-score which indicates that health facilities tend to be more dispersed. The conclusion of this analysis provides an important picture of the distribution of diseases and health facilities in Wonosari City, which can be the basis for more effective public health intervention planning. With the use of spatial medical record data, it is hoped that efforts to control tuberculosis in Wonosari District can be improved. The implications of this research are expected to be one of the solutions for local governments and public knowledge about understanding the spread of TB cases. This research also opens up opportunities for further studies, especially examining the factors and causes of tuberculosis in this region.

MASALIQVol. 5(3)
Universitas Satya Negara Indonesia (ID)
Openalex Percentile: Top 88%
Data Mining and Machine Learning Applications
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