Pemodelan Kasus Tuberkulosis di Indonesia dengan Metode GWPR Guna Mendukung SDGs 2030 - Toha Saifudin; Mochamad Firmansyah; Johanna Tania Victory; Mutiara Aisharezka (Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember, 2024)
Tuberculosis (TB) is the second leading cause of death after coronary heart disease. The bacterium type Humanus of Mycobacterium tuberculosis causes the infectious illnessTB. According to WHO, in 2018 Indonesia had 8% of TB cases, the third highest after India (27%) and China (9%). Therefore, efforts are needed to reduce the number of cases and deaths due to TB, in line with efforts to achieve point 3 of target 3 of the SDGs, namely ending the TB pandemic. This study uses the Geographically Weighted Poisson Regression (GWPR) model approach with the aim of analyzing the factors that influence TB, so that preventive interventions to reduce TB cases can be carried out. The data used in this study is secondary data in the form of data on the number of TB cases in 2018 obtained from the Ministry of Health (Kemenkes RI) and the Central Agency of Statistics (BPS). The observation unit is 34 provinces in Indonesia. Based on the smallest Akaike Information Criteria (AIC) value, the best GWPR model is obtained with Adaptive Bisquare weighting. Each province has a different model. The GWPR model in West Java Province which has the highest number of TB cases in Indonesia is . The results of the analysis show that the number of poor people has a very significant influence in almost all provinces in Indonesia. While this is going on, a considerable impact can be seen in the proportion of unfit homes and the percentage of unsanitary food processing facilities (TPM). Provincial governments in Indonesia can consider the results of modeling with GWPR in formulating strategies to reduce the number of TB sufferers in their regions