In 2020, the COVID-19 pandemic had a significant impact on the healthcare sector in Indonesia, including the number and distribution of healthcare workers. Entering the recovery phase in 2022, various changes occurred in the healthcare employment system, which could affect the number of healthcare workers in different regions, including East Java Province. This study aims to analyze the differences in the number of healthcare workers in East Java between 2022 and 2023 using the Multivariate Analysis of Variance (MANOVA) method. In this study, multivariate normality was tested using Mardia's method, while the homogeneity of the covariance matrix was analyzed through Box's M test. The results of the tests indicated that the data met these assumptions, allowing MANOVA to be properly applied to identify the variables influencing the differences in the number of healthcare workers in East Java. These findings are expected to assist policymakers in designing a more effective distribution plan for healthcare workers.

The ease of investing in the digital era has driven Generation Z to dominate stock market participation, particularly in blue-chip stocks such as PT Bank Rakyat Indonesia Tbk (BBRI). However, stock price fluctuations influenced by macroeconomic factors, regulations, and global market sentiment make it difficult for investors to make accurate decisions. Decisions based on insufficient information pose a significant risk of loss, especially for novice investors. This study proposes a hybrid LSTM-XGBoost approach for predicting BBRI stock prices, combining the strengths of LSTM in capturing nonlinear time series patterns and XGBoost's effectiveness in reducing prediction errors. The model leverages both historical data and feature extraction outputs from the LSTM model. Future stock price values are then predicted by XGBoost using this combined dataset. The Hybrid LSTM XGBoost model outperforms the individual base models in terms of prediction accuracy, achieving an RMSE of 117.89, MAE of 92.45, and MAPE of 2.21%.

Social and economic disparities between regions in Indonesia are still a serious problem, reflected in the high Gini Ratio and disparities in purchasing power. The Consumer Price Index (CPI) is an important indicator in measuring consumption patterns and price pressures between regions. This study aims to cluster 150 districts/cities based on consumption patterns through the CPI using the Agglomerative Hierarchical Clustering (AHC) and Spectral Clustering methods combined with Principal Component Analysis (PCA). The innovation lies in the comparison of the two clustering methods as well as the application of PCA to clarify the data structure. Evaluation with Silhouette Score and Davies-Bouldin Index showed that AHC gave the best results with four representative clusters (Silhouette: 0.76; DBI: 0.32), compared to Spectral Clustering with three clusters (Silhouette: 0.75; DBI: 0.54). Each cluster has different expenditure characteristics, useful for data-driven policy making. These results show that AHC is more effective in capturing interregional variations in consumption.