Infectious disease caused by infection of Mycobacterium tuberculosis is called tuberculosis (TB). A common method in detecting TB is by identifying number of mycobacterium TB in sputum manually. Unfortunately, manually calculation by pathologists take a relatively long time. Previous researches on TB bacteria were still limited to detect the absence or presence of mycobacterium TB in images of sputum. This research aims are identifying number of mycobacterium TB and determining accuracy of classification TB severity by approaching nonparametric Poisson regression model and applying an estimator namely local linear. Steps include processing of image, reducing of dimension by applying partial least square and discrete wavelet transformation, and then identifying the number of mycobacterium TB by using the proposed model approach. In this research, we get deviance values of 28.410 for nonparametric and 93.029 for parametric approaches and the average of classification accuracy values for 4 iterations of 92.75% for nonparametric and 85.5% for parametric approaches. Thus, for identifying many of mycobacterium TB met in images of sputum and classifying of TB severity, the proposed identifying method gives higher accuracy and shorter time in identifying number of mycobacterium TB than parametric linear regression method.

Diarrheal is a disease with the condition of faeces becoming soft or fluid. In general, diarrheal results from food and drinks exposed to viruses, bacteria or parasites. Diarrheal is one of the health problems of people in poor and developing countries. Diarrheal often occurs due to the lack of clean water that pass health requirements. The method used in this research is bivariate negative binomial regression which is a regression method to model a pair of response variables, each of which has a negative binomial distribution and correlates. This study uses 38 secondary data from the East Java Provincial Health Office in 2016 about the number of diarrheal sufferers and the number of certified drink water providers which pass health requirements. The results of this research, there are 4 factors that significantly influence the model of diarrheal sufferers and drink water providers who pass health requirements, that are the percentage of residents with access to proper sanitation, public places that pass health requirements, population literacy rates and food management places tested.