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This research aims to analyze the relationship between the characteristics of teenagers and family functions as well as the understanding of narcotics, psychotropics, and addictive substances on the behavior of teenagers consuming drugs in East Java as a response variable with a binary scale. The data source obtained through secondary data from the Performance Survey and Accountability Program 2019 with the observation unit teenagers aged 10-24 years. The sample used is 4,649. The analytical method used is descriptive statistics, the Chi-square method, and Odds Ratio (OR). The percentage of adolescent consuming drugs is 4.1% descriptively. The relationship analysis shows that the variables significantly related to young people's behavior in consuming drugs are gender, place of residence, level of education, age group, religious values, and psychological consequences. From the OR figures concluded that young males are 3.2 times more at risk of consuming drugs than young females. From the aspect of family function, it can be inferred that the percentage of young substance abusers from families who apply religious values is greater than those who do not. The findings of this research show that the risk of young people from families who practice religious functions becoming substance abusers are 1.61 times more compared to families who do not practice it. The understanding of drugs is not always related to teenagers' behavior in consuming drugs, because those who understand the psychological consequences of substance abuse are also 1.64 times more at risk of using drugs compared to teenagers who do not understand. [ABSTRACT FROM AUTHOR]

The purpose of this study is to propose an appropriate model to predict chemical composition during water purification at the Regional Water Company (PDAM) Surabaya, in order to achieve proper drinking water standards. Drinking water treatment is very expensive, so the model serves as a basis for determining the composition of chemicals used in the water purification process at PDAM Surabaya. This study examines a model of the relationship between the level of clarity of drinking water and the composition of the chemicals used. The government can obtain important benefits from the forecasting model to formulate policies for the company. One of the objectives of developing the estimation method involved in this research is to efficiently determine the exact chemical composition resulting from the water purification process, which will inform the financing and control of water quality. We used a multivariable linear approach for some parametric components, a multivariable Fourier Series approach for some nonparametric components, and a multivariable Kernel approach for semiparametric regression. Using the penalized least square (PLS) approach, a mixed estimator of the Fourier and Kernel Series was obtained with semiparametric regression. The smoothing parameters were selected using a common cross-validation technique (GCV). The performance of this technique was evaluated using the Gaussian Kernel and Fourier Series with data trends in the drinking water clarity level obtained from PDAM Surabaya. The findings showed that this technique performed well, so we recommend that the government conduct an in-depth analysis to determine correct chemical composition so that the cost of water treatment can be minimized. [ABSTRACT FROM AUTHOR]

To date, nonparametric regression studies using a combined estimator model have recently begun to develop significantly. Yet previous research employing this approach is still restricted to models with a single response variable. Hence, this paper offers a novel method to estimate bi-response nonparametric regression using a model that combines Fourier series and truncated spline estimators. This research aims to estimate the regression curve of the proposed model using twostage estimation. The first stage is completed by the penalized weighted least square optimization followed by utilized the weighted least square optimization. We conduct numerical simulations with various sample sizes and correlations to assess the performance of the proposed model. Using generalized cross-validation as a criterion, the best model was obtained from the scenario model with big sample size and strong correlation. Furthermore, compared to uncombined estimators, the proposed model outperformed when applied to a real dataset of the human development index (HDI) education indicator in the East Java Province, Indonesia. [ABSTRACT FROM AUTHOR]

The model developed considers the uniqueness of a data-driven binary response (indicated by 0 and 1) identified as having a Bernoulli distribution with finite mixture components. In social science applications, Bernoulli's constructs a hierarchical structure data. This study introduces the Hierarchical Bernoulli mixture model (Hibermimo), a new analytical model that combines the Bernoulli mixture with hierarchical structure data. The proposed approach uses a Hamiltonian Monte Carlo algorithm with a No-U-Turn Sampler (HMC/NUTS). The study has performed a compatible syntax program computation utilizing the HMC/NUTS to analyze the Bayesian Bernoulli mixture aggregate regression model (BBMARM) and Hibermimo. In the model estimation, Hibermimo yielded a result of ~90% compliance with the modeling of each district and a small Widely Applicable Information Criteria (WAIC) value. [ABSTRACT FROM AUTHOR]