Multivariate Analysis Approach to Factor-Affected Tuberculosis Disease

Abstract

Tuberculosis is a disease caused by infection with the mycobacterium tuberculosis complex. Tuberculosis attack organ besides the lung, such as the pleura, lining of the brain, lining of the heart, lymph gland, bones, joint, skin, intestines, kidney, urinary tract, and genital. This disease is found in densely populated settlements with poor sanitation, lack of ventilation and sunlight and lack of rest. Moreover, the factors that will be analyzed in this research are Population Density (X1), Number of HIV/AIDS (X2), number of toddlers who experience nutrition (X3), Number of toddlers who experience BCG immunization (X4), number of toddlers who get exclusive breastfeeding (X5), Total families with PHBS (X6), number of residents with healthy homes (X7), number of families with clean water facilities (X8), number of families with ownership of latrine sanitation (X9), number of families with have landfills (X10), number of families have management waste place (X11), number of elementary education facilities (X12), Number of junior school education facilities (X13), Number of senior school education facilities (X14), Number of institutions fostered by neighborhood health (X15), Number of Posyandu (X16), Number Life Expectancy (X17), Literacy Rate (X18), Human Development Index (X19), Number of Tuberculosis sufferers (X20). This research aims to analyze what variables influence each other on the prevalence rate of tuberculosis in the city of Surabaya. The method used in this research is a multivariate analysis using factor analysis, cluster analysis, biplot analysis and discriminant analysis. This discriminant analysis determines accuracy by calculating the value (1-APER). The resulting research the Number of HIV/AIDS, number of residents with healthy homes, and Number of families with ownership of Sanitation (latrine, landfills, waste management) have a high correlation with the spread of tuberculosis in Surabaya. Meanwhile, areas with a high rate of tuberculosis are Tambaksari, Wonokromo, Sawahan, and Semampir. The classification analysis accuracy level was 90.32% and the accuracy of the resulting model or discriminant function was very high. So that discriminant analysis can be used for predicting the accuracy of tuberculosis prevalence rates.