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.

One of the most commonly encountered issues in large cities is air pollution. As a major city, Bandung also experiences the same problem. This issue arises due to the increasing levels of human activity. This contributes to elevated levels of pollutants in the atmosphere, which can impact human life and ecosystems. This research is intended to map the regions in Bandung based on their air quality. This study used ambient air quality measurement results from Bandung, which included PM₁₀, PM_2.5, dust, SO₂, CO, and NO₂. This ambient air quality measurement was conducted by the Department of Environment and Hygiene in Bandung. The research methodology utilized in this study was multidimensional scaling analysis. The outcomes of the examination carried out utilizing the multidimensional scaling technique reveal a clustering of regions in Bandung, West Java, based on their air quality. According to the research findings, the locations were grouped into four quadrants, each with different air quality characteristics. Some locations showed high similarity, while others did not exhibit similarity with other groups. These findings can be used for policy-making and improving air quality in Bandung. Conclusions were drawn from the formed groups, where each group had high similarity among its members, but differed from the members of other groups. Among all observed locations in Bandung City, there were areas that were most similar when viewed based on the distance between objects, namely Punclut St. and KPAD Sarijadi; Soekarno Hatta St. (in front of Astra Bizz) and Elang St.; and Buah Batu St. (in front of STSI/ISBI) and Bunderan Cibiru.

The stock price of PT Indika Energy Tbk (INDY) reflects the dynamics of Indonesia’s energy sector, which is heavily influenced by global coal price fluctuations, national energy policies, and geopolitical conditions. This study aimed to develop an accurate forecasting model to predict the movement of INDY stock prices using a hybrid machine learning approach called CNN-BiGRU-AM. The objective was to generate future forecasts of INDY stock prices based on historical data from 28 August 2019 to 24 February 2025. The method applied a hybrid model combining a Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and an Attention Mechanism (AM) to address the nonlinear, volatile, and noisy characteristics of stock data. The results showed that the CNN-BiGRU-AM model achieved high accuracy with a Mean Absolute Percentage Error (MAPE) below 3%, indicating its effectiveness in capturing long-term patterns. The CNN helped extract local features and reduce noise, the BiGRU captured bidirectional temporal dependencies, and the Attention Mechanism allocated weights to the most relevant historical information. The model remained robust even when stock prices were sensitive to external factors such as global commodity trends and geopolitical events. This study contributes to providing more accurate forecasting solutions for companies, investors, and stakeholders in making strategic decisions. It also enriches the academic literature on the application of deep learning techniques in financial data analysis and stock market forecasting within a complex and dynamic environment.

The cigarette commodity is the second largest contributor to the food poverty line. Several aspects imply that poor people consume cigarettes despite having a minimal income. In this study, we are interested in investigating factors influencing poor people to be active smokers. Since the consumption number is a set of count data with zero excess, we have an overdispersion problem. This implies that a standard Poisson regression technique cannot be implemented. On the other hand, the factors involved in the model need to be selected simultaneously. Therefore, we propose to use a zero-inflated negative binomial (ZINB) regression with a minimax concave penalty (MCP) to determine the dominant factors influencing cigarette consumption in poor households. The data used in this study were microdata from the National Socioeconomic Survey (SUSENAS) conducted in March 2019 in East Java Province, Indonesia. The result shows that poor households with a male head of household, having no education, working in the informal sector, having many adult household members, and receiving social assistance tend to consume more cigarettes than others. Additionally, cigarette consumption decreases with the increasing age of the head of household.

Produk Domestik Bruto (PDB) memiliki peran yang sangat penting dalam untuk mengerti kondisi perekonomian negara. Penelitian ini bertujuan untuk memodelkan variabel PDB dengan mempertimbangkan variabel RTGS (Real Time Gross Settlement). Akan tetapi, data yang digunakan dalam penelitian ini tidak memenuhi asumsi stasioner. Metode yang digunakan pada penelitian ini adalah Vector Error Correction Model (VECM) yang merupakan salah satu model multivariat runtun waktu yang merupakan bentuk Vektor Autoregresive terestriksi dengan data yang tidak stasioner namun kombinasi liniernya memiliki kointegrasi. Hasil analisis model tersebut adalah terdapat kointegrasi antara PDB dan RTGS. Parameter model diestimasi dengan hasil estimasi jangka panjang RTGS signifikan sebesar -0,8828. Dari analisis kausalitas Granger terdapat hubungan satu arah PDB dengan RTGS. Akurasi model ditunjukkan oleh nilai MAPE sebesar 0,10%.

A study of a disease in an area is periodically measured. In this study, time is nested within the individual so it derives a data with nested structure. Ordinary regression model is not appropriate used because it does not consider the existence of nesting effects. Multilevel model usually used for hierarchical data, because it is not ignore the effect of the data structure. According to Sneijder and Bosker, nested data can be viewed as hierarchical data, so it can be analyzed using multilevel model. Diarrhea case in an area is affected by clean water, hand washing habits, healthy latrines and healthy house. This data is measured periodically from 2012 to 2016. Multilevel model is applied to diarrhea case data in Bandung city which has nested structure. The results obtained from the analysis is that the data used has a hierarchy structure so that it can be analyzed using a multilevel model. In addition, the random intercept model used is very fit with three significant variables. In general, the result for this case is that the occurrence of cases of diarrhea in Bandung influenced by clean water and hand wash habits with the variation between districts.