Geoinformatics of Tuberculosis (Tb) Disease in Jakarta City Indonesia

Abstract

Tuberculosis (TB) disease has still become the leading cause of morbidity and mortality in Indonesia. TB had a relationship between human and their environment, especially in an urban area. The urban area had the highest population and density, that is the way the accurate information about the urban environment of the TB area is critical. The research objective to representing characteristic spatial of TB patients lived in Jakarta year 2017. The research used a method using visual interpretation from superimposed spatial data. Spatial data collection from google earth data, household area (RW boundary), and TB patient addressed from the hospital. The total TB patients in Jakarta City year 2017 in total 114 from a hospital and distributed in 108 household areas (RW boundary). The result of geoinformatics of the urban environment, first building population, building density, vegetated covered, and distance from the main road. The TB area characteristic had two classified high and low, based on total patient TB disease in a household area (RW boundary). The research concluded of the geoinformatics of TB disease in Jakarta City year 2017 with highly classified is high building population and building density, lowest vegetated covered (high temperature), and near the main road. The research also uses the PAM algorithm to cluster the patient and resulted in four clusters with differ mostly in building density, building temperature, and the distance to the nearest river.

Development and validation of a structured questionnaire for assessing risk factors of medication non-adherence among pulmonary tuberculosis patients in Indonesia

“…The questionnaire was suitably developed to address the complexities of the diverse regions in Indonesia and the unique healthcare challenges encountered. Indonesia has been reported to show significant regional disparities in healthcare infrastructure, patient attitudes, and socioeconomic influences ( Erawati and Andriany, 2022 ; Siswantining et al, 2020 ; Pratiwi et al, 2020 ). Considering these disparities, the questionnaire was designed to be adaptable and relevant across different areas of the country, but some regional customization was required before the broad implementation ( Mahmudiono and Laksono, 2021 ).…”

Geoinformatics of Spring Water Quality in Small Village

“…Geoinformatics seems popular in Europe, whereas Geographic Information Science usually indicates a Geography background (Goodchild, 1992). Siswantining et al (2020) made a paper on Geoinformatics for Tuber Colosis (Tb) in Jakarta regarding information and analysis of the distribution characteristicsstic of that disease. In other research, Wibowo (2005) found the environmental threshold as the quality of life using geospatial or geoinformatics analysis.…”

Geographically Weighted Panel Regression With Fixed Effect for Modeling the Number of Infant Mortality in Central Java, Indonesia

“…This paper models infant mortality in Central Java using the GWPR model. Also (Siswantining et al, 2020) applied spatial analysis for modelling tuberculosis disease in Jakarta city, Indonesia. The dependent variable is the infant mortality rate in Central Java in 2015 up to 2017, while the independent variables used include the percentage of pregnant women who visit K1; Percentage of pregnant women who received Fe3 tablets; Percentage of deliveries assisted by health workers; The percentage of households that are clean and healthy.…”