Accurate dengue forecasting is essential for sustainable public health planning, especially in tropical regions where the disease remains a persistent threat. This study evaluates the predictive performance of seven modeling approaches—Seasonal Autoregressive Integrated Moving Average (SARIMA), Extreme Gradient Boosting (XGBoost), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Convolutional LSTM (CNN–LSTM), and a Bayesian spatiotemporal model—using monthly dengue incidence data from 2009 to 2023 in Bandung City, Indonesia. Model performance was assessed using MAE, sMAPE, RMSE, and Pearson’s correlation (R). Among all models, the Bayesian spatiotemporal model achieved the best performance, with the lowest MAE (5.543), sMAPE (62.137), and RMSE (7.482), and the highest R (0.723). While SARIMA and XGBoost showed signs of overfitting, the Bayesian model not only delivered more accurate forecasts but also produced spatial risk estimates and identified high-risk hotspots via exceedance probabilities. These features make it particularly valuable for developing early warning systems and guiding targeted public health interventions, supporting the broader goals of sustainable disease management.

This study investigates regional heterogeneity and spatial interdependence in digital skills mismatch across Indonesia by constructing a Digital Skills Supply–Demand Ratio (DSSDR) from the Indonesia Digital Society Index (IMDI). In line with SDG 10 (Reduced Inequalities) and SDG 4 (Quality Education), the study aims to provide policy-relevant evidence to support a more inclusive and balanced digital transformation. Using district-level data and spatial econometric models (OLS, SAR, and the SDM), the analysis evaluates both local determinants and cross-regional spillover effects. Model comparison identifies the Spatial Durbin Model as the best specification, revealing strong spatial dependence in digital skills imbalance. The results show that most local socioeconomic and digital readiness indicators do not have significant direct effects on DSSDR, while school internet coverage exhibits a consistently negative association, indicating that digital demand expands faster than local supply. In contrast, spatial spillovers are decisive: a higher share of ICT study programs in neighboring regions improves local DSSDR through knowledge and human-capital diffusion, whereas higher GRDP per capita in adjacent regions exacerbates local mismatch, consistent with a talent-attraction mechanism. These findings demonstrate that digital skills mismatch is a spatially interconnected phenomenon driven more by interregional dynamics than by local conditions alone, implying that policy responses should move beyond isolated district-level interventions toward coordinated regional strategies integrating education systems, labor markets, and digital ecosystem development. The study contributes a spatially explicit, supply–demand-based framework for diagnosing regional digital inequality and supporting more equitable and sustainable digital development in Indonesia.

Social media has the potential to serve beneficial purposes. The abundance of uploaded content and responses from the public generates various opinions, allowing them to be identified as positive or negative regarding the portrayal of Bandung Regency. This research aims to analyse the classification and frequency of words for each sentiment expressed by X (Twitter) users regarding Bandung Regency. The research employs the Support Vector Machine (SVM) method. We expect the results to aid in formulating governmental programmes for Bandung Regency. The research revealed that the SVM model, which uses the Sigmoid kernel function with parameters $C$ = 10 and gamma ($\gamma$) = 1, is the most optimal sentiment classification model for handling an imbalanced dataset. This model achieved an 83.01% negative recall value. Furthermore, frequent words appearing in both classes indicate that several positive opinions about Bandung Regency exhibit similar dominance, except for football dominance in negative opinions. This research pertains to the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities) and SDG 16 (Peace, Justice, and Strong Institutions). The suggested technique facilitates evidence-based policy reviews, transparent governance, and enhanced responsive public services by analysing public sentiment regarding local government performance. The results illustrate how social media analytics can aid local governments in assessing popular sentiment and pinpointing areas for policy response.

Probabilistic earthquake forecasts can support urban preparedness only when catalog quality, parameter uncertainty, predictive performance, and the limits of interpretation are considered together. We examine the Lembang Fault in West Java by linking a regional Bayesian ETAS analysis with a city-scale preparedness-prioritization assessment for Bandung City. The audited catalog within 50 km of the study area contains 1983 earthquakes, including 1797 shallow events at depths of 30 km or less. Because the catalog combines several reported magnitude types, only 114 events could be confidently expressed on a consistent moment-magnitude scale (M_w). Rather than forcing uncertain magnitude conversions, the primary 2018–2024 analysis retains the original reported catalog magnitudes, denoted here as M_cat, and includes 562 shallow events above a conservative working threshold (M₀ = 2.5). A smaller subset of 36 events that can be expressed consistently as moment magnitude (M_w ≥ 3.4) is used for a sensitivity analysis. Four MCMC chains showed satisfactory numerical convergence. The long-term asymptotic branching diagnostic had a median of 7.09, whereas the finite-observation-window reproduction diagnostic had a median of 0.845, supporting interpretation of the model for conditional short-horizon forecasting rather than long-term stationary behavior. The primary model predicts a median of six earthquakes over 30 days, with a 95% predictive interval of 0–23 events and a 96.3% probability of at least one event. In an out-of-sample test for 2024, the model substantially overpredicts the central event count, with a median prediction of 149 events compared with 72 observed, although its spatial forecast performs better than a uniform spatial benchmark. For Bandung City, the 30-day seismicity forecast (F) is combined with 2024 population exposure (E), a provisional socioeconomic vulnerability component (V), and critical-health-facility exposure (C) in an equal-weight Dynamic Preparedness-Priority Index (DPPI). Bojongloa Kidul, Coblong, and Babakan Ciparay have the highest baseline DPPI values. DPPI is intended to identify relative preparedness priorities and should not be interpreted as an estimate of absolute seismic risk, expected damage, or economic loss. Overall, the framework provides a transparent connection between probabilistic seismicity forecasting and urban preparedness planning while keeping uncertainty, data limitations, and model boundaries explicit.

This study introduces a spatiotemporal framework at the district level in Indonesia to examine and forecast stunting prevalence. The empirical analysis draws on data from 514 districts observed over 2022–2024, with short-term projections extended to 2025–2027 in line with the SDG 2.2 agenda. The modeling methodology is based on a Bayesian spatiotemporal formulation with the SPDE-INLA method. Instead of handling spatial and temporal lags separately, the model simultaneously incorporates them to reflect dependencies that change across both dimensions. This structure facilitates a more flexible representation of underlying risk dynamics. To improve prediction performance, we augment the baseline model with a hybrid component. Specifically, residual variation from the Bayesian specification is further explored using machine learning methods, providing an additional layer of adjustment. Spatial dependence is assessed through three alternative weighting schemes—KNN, Queen contiguity, and distance-based matrices—which are compared prior to selecting the final specification. The empirical specification includes nine key predictors within a semi-parametric framework. Several covariates are allowed to depart from strict linearity by accommodating time-varying effects. Three algorithms were evaluated during the prediction process to determine their abilities to capture the residual structure: XGBoost, Random Forest, and Elastic Net. Spatiotemporal clustering is examined through exceedance probabilities, resulting in the identification of seven unique cluster patterns. The findings consistently indicate that poverty is the main factor influencing stunting dynamics, with evident regional spillovers and temporal variations. Persistent hotspots are primarily located in eastern Indonesia. From a predictive standpoint, the hybrid specification—particularly the variant based on XGBoost—delivers the most stable performance. The forecast results indicate a gradual reduction in stunting prevalence throughout the forecast period. This study establishes persistent geographic inequalities in child nutrition risk and translates them into district-specific intervention priorities, providing decision-support information to further SDG Target 2.2 and its relationships with SDGs 1, 3, 4, and 6.

The Sustainable Development Goals (SDGs) are a series of 17 goals fixed by the United Nations and adopted by 193 countries in 2015, including Indonesia. By 2030, to end all forms of malnutrition, targeting on stunting and wasting in children under 5 years of age is one of the targets from Goals Number 2. One affordable source of protein and nutrition used as a solution to overcome malnutrition problems such as stunting is eggs. Egg price modeling was carried out to see the affordability of prices for the community. Weekly dataset of egg price in NTT Province from 2018 to 2023 used to modeling with Convolutional LSTM. The Convolutional LSTM components are Adam optimizer, ReLU activation function, Huber loss function, with batch size and neurons of 32. The MAPE value obtained from the model is relatively small, with MAPE for training, validation, and testing of 1.97%, 1%, and 1.19% respectively. The results of egg price forecasting for December 11, 2023, to January 8, 2024, show that egg prices tend to continue to decline per week. Thus, a decrease in egg prices can be a good thing in providing more affordable nutrition for the community.