In Indonesia malaria is found to be widespread in all islands with varying degrees and severity of infection. Based on the Annual of Parasite Incidence (API) in Eastern Indonesia, Malaria is a disease that has a high incidence rate. The three provinces with the highest APIs are Papua (42.64%), West Papua (38.44%) and East Nusa Tenggara (16.37%). Spatial aspects are considered important to be studied because the spread of disease through mosquitoes is strongly influenced by fluctuating climate. The purpose of this study is to determine the potential factors that influence the incidence of Malaria disease in the province of Papua in 2013 by looking at aspects that are the focus of attention in spatial epidemiology. The methods used in analyzing the area are Simultaneous Autoregressive (SAR) and Conditional Autoregressive (CAR) models with a spatial weighting matrix up to second order. The result shows the average monthly wind velocity, average monthly rainfall, and malaria treatment with government program drugs by getting ACT drugs are substantial factors in determining the incidence number of Malaria in Papua based on the lowest AIC value for the second-order of CAR model. While the SAR model, in this case, has no spatial influence. By knowing the potential factors that influence the incidence of malaria, the Papua Province through the Health Office can take more effective preventive measures to reduce the number of malaria incidents.

We setup boundary value problems (BVPs) for exponentially graded media. The BVPs are governed by a diffusion convection-reaction (DCR) equation of spatially varying coefficients (with an anisotropic diffusion coefficient). A boundary element method (BEM) is utilized to find numerical solutions to BVPs. The numerical solutions obtained verify the validity of the analysis used to derive the BEM with accurate and consistent solutions. The computation shows that the BEM procedure elapses very efficient time in producing the solutions. In addition, the results show the effect of anisotropy and inhomogeneity of the media on the solutions. To illustrate the application, an example of a layered material is presented.

Pneumonia is a disease that causes inflammation of the lungs and is one of the most common diseases infecting toddlers. As a directly infectious disease, there is a possibility of the influence of location diversity on the number of pneumonia sufferers. Robust Geographically and Temporally Weighted Regression (RGTWR) is a method used to model data by considering the heterogeneity of location and time and to overcome outliers in the data. The data used is the number of pneumonia sufferers aged under five and the factors that are thought to influence it, namely the number of health centers, population density, percentage of children under five with complete basic immunizations, percentage of children under five who are exclusively breastfed 0-6 months, and percentage of poor people. This study was conducted to model pneumonia sufferers under five and to find out the factors that significantly affect the number of sufferers in each observation. RGTWR produces an optimal model with an R2 value of 99.9997%, a Mean Absolute Deviation of 21.6852, and a Median Absolute Deviation of 6.9661 compared to the Geographically and Temporally Weighted Regression model. Variables number of puskesmas, percentage of infants with complete basic immunization, and percentage of poor population are factors that influence the number of pneumonia sufferers under five in most locations in 34 provinces and 5 years of observation.

Makassar is one of the big cities in South Sulawesi Province which has a high incidence of dengue fever (DF). The number of DF incidence cannot be reduced effectively, even though various efforts have been made. The aim of this study is to analyse the factors that influence the spread of DF incidence using the Conditional Autoregressive (CAR) method. The spatial analysis of DF incidenceis also done using Moran'sIndex. In addition, a map of the spread of DF incidence is made based on the relating factors. This study used the number of DF incidence in Makassar city in the period 2012-2017. The results show that the number of population and socioeconomic conditions as malnutrition families describes substantially the spread of DF incidence in Makassar city during the period of 2012-2017. The DF distribution has positive spatial autocorrelations from 2012-2017. The pattern of the spread of DF incidenceis clustered. Some sub-districts included in the category prone to the spread of DF incidence are Manggala, Rappocini, Tallo and Biringkanaya. For this reason, it is recommended that the health office of Makassar city should implement the regional-based disease management to eradicate and prevent the occurrence of DF incidence.

Hypertension is a non-communicable disease that is characterized by an increase in systolic and diastolic blood pressure of more than 140 mmHg and or 90 mmHg. Hypertension needs to get more attention the condition is because hypertension will cause complications in the target organs and this disease does not appear to show significant symptoms at the beginning of the disease because it is called "silent disease". The study discusses the integration method of resampling and boosting in predicting hypertension status using the C5.0 algorithm. Classification of the C5.0 Algorithm by applying to resample increases performance specificity and AUC. Random oversampling (ROS) increased the specificity by 95.67% and AUC increased by 91.11%. Random over-under sampling (ROUS) increased specificity by 88.84% and AUC increased by 87.13%. In addition, applying boosting to the C5.0 algorithm that has been reapplied increases the accuracy performance. Random oversampling (ROS) increased accuracy by 93.86% and random over-under sampling (ROUS) increased accuracy by 89.98%. The response variables that contributed the most were high cholesterol and heart problems. The application of resampling and boosting to the contribution of high cholesterol and heart problems always topped the list.