A nation's health status can be determined by the availability of healthcare services, which is a crucial part of human life. Since 2011, health facilities in Indonesia have been acknowledged as an important health indicator. This study uses correspondence analysis and spatial visualization to look at the primary healthcare facilities in each region of Indonesia. The analysis makes use of information from Indonesia's province-level data on the number of Regions with health facilities in 2021, along with six different types of medical facilities: hospitals, maternity hospitals, polyclinics, health centers, sub-district health centers, and pharmacies. To show the spread of medical facilities in Indonesia, a spatial representation is also produced. In comparison to provinces on other islands, the analysis reveals that the provinces on Java Island have a more varied and adequate distribution of healthcare facilities. Health facilities on other islands' provinces, however, are only focused on public health and sub-district public health. The spatial representation gives a clear picture of the distribution of medical services and draws attention to the distinctions across Indonesia's regions and islands. The geographical visualization offers a thorough perspective of the distribution of health care facilities, and this study delivers insightful information about how health care facilities are distributed in Indonesia. Future research and policy decisions targeted at enhancing Indonesia's healthcare system can be informed by these findings.

The welfare of people has always piqued our interest, and it remains the primary goal of nations around the world in their development endeavors. To effectively drive development efforts, it is critical to understand the diverse welfare features that exist in different locations. Thus, the purpose of this statistical analysis is to classify Indonesian provinces based on a comprehensive set of People's Welfare Indicators, which includes Population Density (PD), Percentage of Poor Population (PPP), Life Expectancy Rate (LER), and Average Years of Schooling (AYS). The methodology used in this study is Hierarchical Cluster Analysis, which employs five distinctive techniques: Single Linkage, Average Linkage, Complete Linkage, Ward's Linkage, and the Centroid Method. The data for this study was obtained from reliable secondary sources, notably the official website of the Central Bureau of Statistics (BPS), and it provides insights on Indonesia's welfare picture in 2021. The average linkage approach shows as the most suitable of the five hierarchical cluster analysis methods used, with the closest cophenetic correlation to 1. The analysis reveals three distinctive clusters within the Indonesian context. Cluster 1 demonstrates a tendency toward low PWI (People's Welfare Index) status, while Cluster 2 exhibits a notably high PWI status. Cluster 3 occupies an intermediate position, characterized by moderate PWI status. These findings not only give useful classification but also act as an important reference point for the Indonesian government. They provide an in-depth insight into each province's distinct welfare features, supporting smart resource allocation and prioritizing aid distribution in regions of highest need. As a result, this research is an essential resource for creating equitable and effective policies and methods to improve people's well-being throughout Indonesia.

Tuberculosis (TB) is still a major public health concern in many regions of the world, including Indonesia's West Java Provinces. Accurate TB risk factor prediction can enhance overall TB control efforts by directing focused therapies. In this study, utilizing a combination of Negative Binomial Mixed Models (NBMMs) and Feed-Forward Neural Networks (FFNNs), we offer a unique method for the predictive modeling of TB risk variables. A variety of sociodemographic, behavioral, and environmental factors that are known to be linked to TB are included in the dataset utilized in this investigation. To correct for overdispersion and include both fixed and random effects in the model, we first fitted an NBMM major problem in epidemiological investigations is modeling count data with overdispersion, and the NBMM component of the model offers a versatile and effective framework for doing so. Following that, we include an FFNN component in the model, which helps us to detect relevant predictive features and alter the model's weights accordingly. Backpropagation methods are used by the FFNN to adjust model parameters and enhance accuracy. The resulting Negative Binomial Mixed Model Neural Network (NBMMNN) model has a high accuracy value of up to 0.944. Our research suggests that the NBMMNN model outperforms conventional models that are frequently used to predict TB risk factors. By contrast to simpler models, the NBMMNN model can capture complicated and nonlinear interactions between predictors and outcomes. Additionally, the inclusion of random variables in the model enables us to take into account potential sources of variability in the data as well as unmeasured confounding. This work emphasizes the opportunity to enhance TB risk prediction and control efforts by integrating NBMMs with FFNNs. In West Java Provinces and other comparable contexts, the NBMMNN model might be a helpful tool for identifying and resolving TB risk factors, guiding targeted interventions, and enhancing overall TB control efforts.