Introduction/Main Objectives: Stunting is the impaired growth and development that children experience from poor nutrition, repeated infection, and inadequate psychosocial stimulation. Background Problems: Based on data from the National Nutrition Status Survey (SSGI) in 2022, the prevalence of stunting in Indonesia was 21.6%, which is still above the WHO standard of below 20%. Novelty: This study was conducted with the aim of analysing the factors that influence the stunting prevalence rate in Indonesia using multi-predictor truncated spline nonparametric regression. Research Methods: The research data is secondary data taken from Health Statistics 2022 with response variables in the form of stunting prevalence. Finding Result: Based on the analysis, the best model to model the stunting prevalence rate is a multi-predictor truncated spline with three knots. In addition, it was found that four predictor variables which are the percentage of infants under 6 months old receiving exclusive breastfeeding, the average age of a mother's first pregnancy, the percentage of married women aged 15-49 using contraception, and the percentage of mothers who gave birth to a live child in the past two years and initiated early breastfeeding had a significant effect simultaneously and partially on the stunting prevalence rate in Indonesia.

Breast cancer is the most common cancer in women and the leading cause of cancer-related death in Indonesia. Analysis of survival data is important for improving the treatment and care of breast cancer patients. This study aims to estimate the parameters, find the survival function, and hazard function of breast cancer patients using a parametric method with an exponential distribution. Previous studies have shown that the Maximum Likelihood Estimation (MLE) method is suitable for estimating the survival function from exponential survival data by censoring. In this study, the exponential distribution was found to be the best for data on breast cancer patients from Surabaya Ontology Hospital. The estimated parameters are θ = 33.9157, and the survival function is calculated using  The estimated hazard function for patient death or failure is 0.0295. The results of this study can contribute to the development of better treatment and care strategies for breast cancer patients. However, further research is needed because this study only used monthly time units.

Rapid technological advancements have significantly transformed the global labor market, impacting various industries and workers in Indonesia who may need to be adequately prepared to adapt. The current landscape demands individuals who can acquire knowledge quickly and adapt to modern technologies. The unemployment rate in Indonesia, especially among fresh graduates, is still a concern. Lack of motivation and interest in finding a job and high expectations of working conditions contribute to this problem. This research aims to address the gap in research by using the Finite Mixture Partial Least Squares (FIMIX-PLS) approach to examine the segmentation of fresh graduate characteristics about their interest and motivation in finding a job. Segmentation based on latent variable relationships in the structural model can be overcome with Finite Mixture Partial Least Square (FIMIX-PLS) to identify more homogeneous characteristics. This research analyzes explicitly the impact of compensation, work environment, and company reputation on the interest and motivation of fresh graduates in finding a job. This research resulted in the best segmentation of two segments: the 1st segment at 77.8% (265 samples) and the 2nd segment at 22.2% (75 samples).

Research on survival data is quite important. Many modeling problems can be made related to survival data. One of the survival data methods is the Type III Censored method, which is a type of censoring that is limited by time, with individuals entering the study at different times during a certain period of time. In this study, the research focus is to obtain point estimates of the Weibull distribution parameters using the Maximum Likelihood Estimator method. The method is used to examine the survival data of Weibull distribution censored type III on secondary data of patients with lung cancer at Dr. Kariadi Hospital Semarang. Based on the results of the analysis, the Weibull distribution with a point estimator for the scale parameter is α̂ = 94.875997 and the shape parameter is β̂ = 1.638958.

Survival analysis is a research method that studies the duration individuals or experimental units endure against events like death, disease, recovery, or other experiences. This study employs a semi-parametric survival analysis model using the Cox proportional hazards regression method to identify factors such as age, gender, marital status, and education influencing how long employees stay with a company before resigning. The aim is to describe and interpret significant factors affecting employee resignation using the Cox Regression method. The results indicate that age significantly influences employee tenure. The average tenure is eight years. The probability of an employee still working at age 32 for up to eight years is 0.0057, while the likelihood for an employee who has worked more than eight years at age 32 is 0.9943. The study uses secondary data on the tenure of 521 employees, analyzed with the Cox proportional hazards regression method. The data, however, has limitations due to type III censoring, where some subjects leave observation, resulting in incomplete data. The study concludes that age significantly impacts employee tenure. Younger employees tend to explore career opportunities, while older employees seek stability, pension benefits, and a comfortable work environment.

Bayes estimation of a two-parameter exponential distribution and its implementation