Indonesia still has many fundamental problems in terms of government, religion, socio-cultural issues and in particular the problem of poverty. Java is one of the Indonesian islands with 6 provinces and the largest population among the other islands of Indonesia, therefore the problem of poverty on Java must be given greater attention. Specific data on poverty in Java must be collected from several populations. In parallel with the development of statistics, the generalization of the population is not only based on the results of a study but also requires a lot of research, so that the development of statistical methods is necessary. The meta-analysis (MA) is a method that can combine a variety of studies to enable optimal research. The meta-analysis associated with Structural Equation Modeling (MASEM) is one of the developments of statistical methods to examine the influencing factors. The Structural Equation Modeling (SEM) model including poverty as an endogenous variable and economic factors, human resources (HR) and health as exogenous variables have been studied in each province of Java. From the results of these studies, will be developed by the MASEM method through the generalized least squares (GLS) approach in order to show the adequate poverty model on Java. The results of T-test model with the implementation of the MASEM GLS on MatlabR2018 (GLS-M) have shown that poverty was affected by health and HR with coefficient being -0.2761 and -0.2111 but was not affected by Economy. The accuracy of the poverty model, influenced by exogenous variables of economy, health, and human resources, is 50.6% with a chi-square value of 637.0749. While the performance of MASEM with TSSEM on RStudio is better than GLS-M or GLS on RStudio at the model fitting stage.

East Java statistics indicate that the total population of East Java Province is increasing every year. The increase in the population is followed by an increase in social problems, including unemployment. The unemployment rate can affect the economy of the society. Efforts must be made to reduce it by improving the quality of society through factors related to education and health. According to data from East Java in 2017, East Java's education has a significant positive direct effect on the East Java economy of 0.343%, while indirectly through unemployment has an insignificant effect increased by 0.0021%. In addition, health has no significant direct and indirect effect on the East Java economy with coefficients of 0.078% and -0.0023%, respectively.

The fixed effect meta-analytic structural equation modeling (MASEM) model assumes that the population effect is homogeneous across studies. It was first developed analytically using Generalized Least Squares (GLS) and computationally using Weighted Least Square (WLS) methods. The MASEM fixed effect was not estimated analytically using the estimation method based on moment. One of the classic estimation methods based on moment is the Generalized Method of Moments (GMM), whereas GMM can possibly estimate the data whose studies has parameter uncertainty problems, it also has a high accuracy on data heterogeneity. Therefore, this study estimates the fixed effect MASEM model using GMM. The symmetry of this research is based on the proof goodness of the estimator and the performance that it is analytical and numerical. The estimation results were proven to be the goodness of the estimator, unbiased and consistent. To show the performance of the obtained estimator, a comparison was carried out on the same data as the MASEM using GLS. The results show that the estimation of MASEM using GMM yields the SE value in each coefficient is smaller than the estimation of MASEM using GLS. Interactive GMM for the determination of the optimal weight on GMM in this study gave better results and therefore needs to be developed in order to obtain a Random Model MASEM estimator using GMM that is much more reliable and accurate in performance.

High and sustainable economic improvement is the main requirement for continued economic development and prosperity. According to the BPS data, the province of Java is showing that the population of the province of Java is increasing year by year. The increase in the population also increases the unemployment rate. Therefore seek to reduce unemployment by improving the quality of education. The purpose of this study is to analyze the educational factors of the economy of society, directly and indirectly, through the unemployment rate in East Java in 2017. According to the results of the analysis, education has a direct and significant effect on improving the economy of society. the inhabitants of East Java with a coefficient increased by 67.6%. The improvement of education can be achieved by increasing the School Participation Rate, Literacy rate and Highest level of education, so that its economy is based on the GRDP rate and the implicit GDP index also increase. But education has no significant effect on the economy of society indirectly through unemployment, as unemployment has no significant effect on the economy of the society.

Stunting remains one of Indonesia’s most persistent public-health problems, and the nationalprevalence conceals subtantial variation across the country’s 38 provinces. This study modelsseven latent constructs behind stunting, antenatal care, early initiation of breastfeeding,immunization, sanitation, exclusive breastfeeding, low birth weight, and stunting using PartialLeast Squares Structural Equation Modeling on 2023–2024 provincial health-profile datapublished by Indonesia’s Ministry of Health, and then applies Finite Mixture Partial LeastSquares (FIMIX-PLS) to detect unobserved heterogeneity across provinces and to translate theresulting segments into a thematic map. The measurement model satisfied every recommendedvalidity and reliability threshold, with an Average Variance Extracted between 0.835 and0.932 and both Cronbach’s alpha and composite reliability above 0.80. In the pooled nationalmodel, low birth weight was the only construct with a statistically significant direct path tostunting (β = 0.540, p .001); antenatal care showed no significant direct path to stunting(β = −0.143, p = .678) but exerted a significant total effect (β = −0.365) entirely through afully mediated pathway via low birth weight (indirect effect β = −0.263, p = .022), togetherexplaining 63.0% of the variance in stunting. Model-selection criteria favored a three-segmentsolution comprising 21, 10, and 7 provinces, dominated respectively by sanitation-related,birth-weight-related, and breastfeeding-related structural pathways. Mapping these segmentsgeographically shows that provinces sharing a dominant pathway are not scattered at randombut tend to cluster by region, indicating that stunting-reduction programs should be tailoredto each segment’s dominant pathway rather than applied uniformly across the whole country.

Infrastructure development is important for reducing regional disparities, but Indonesian provinces differ in economic conditions, human resources, and regional characteristics. This study examines the relationships between the Economy, Human Resources, and Regional Characteristics and Infrastructure in the 38 provinces of Indonesia, based on secondary data from Statistics Indonesia (BPS) for 2024 and 2025. To examine the structural relationships, Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed, and to investigate unobserved heterogeneity, Finite Mixture PLS (FIMIX-PLS) was used together with ImportancePerformance Map Analysis (IPMA) to determine the priorities within the segments. The model accounted for 82.3% of the variance in Infrastructure (R2 = 0.823). Yet none of the structural paths were found to be statistically significant at the 5% level: for Economy (t = 1.195, p = 0.232), for Human Resources (t = 0.895, p = 0.371), and for Regional Characteristics (t = 1.928, p = 0.054). FIMIX-PLS identified a two-segment solution, with the final partition assigning 27 provinces to Segment 1 and 11 provinces to Segment 2. IPMA showed different patterns across segments: Regional Characteristics had the highest importance in Segment 1, while Economy ranked highest in Segment 2. Regional Characteristics also had the lowest performance in both segments. As this construct represents disaster exposure rather than development quality, the results highlight the need to consider disaster risks and economic differences when setting infrastructure priorities. However, the findings remain exploratory because none of the overall structural paths were statistically significant, and the segmentation is exploratory.