The high number of domestic violence cases in Indonesia indicates the need for a scientific approach to understand the dynamics of the spread and more systematic control efforts. In this study, model is developed into six compartments, namely ShIRSvHC. In the model, the equilibrium point and stability of the equilibrium point are sought, as well as numerical simulations using the fourth order Runge-Kutta method and the simulation results are compared with actual data for 2020-2024. Evaluation of model accuracy is done by calculating the relative error. The simulation results show that the sub population has decreased every time with a population of about 268,030 women. The I population experienced an increase at the beginning of the period and experienced a significant decrease in the following year, the R population experienced an increase at the beginning to the middle of the period and for the and H, C populations experienced an increase every time.

Business actors require banking services that effectively support their financial activities. The variety of available banks requires institutions to understand customer preferences in order to develop competitive strategies. This study aims to determine the optimal strategy that banks may implement based on the preferences of micro, small, and medium business actors using game theory. The research employed a two-stage data collection process. The first stage involved 30 respondents to identify the two most preferred banks, namely Bank A and Bank B. The second stage involved 108 respondents, determined through the Yamane formula, to evaluate eight strategic banking attributes. Payoff values were calculated using the difference in preference probabilities for each attribute. Game theory analysis used a two-person zero-sum game with pure strategies. The results indicate that Bank A optimal strategy is ATM availability, while Bank B optimal strategy is loan interest rates. These findings may assist banks in enhancing their services according to business actors’ preferences.

Nutritional problems contribute to 50% of deaths among children under five, particularly in low- and middle-income countries. One of the most common issues in Indonesia is stunting, a condition where a child's height falls below the standard for their age. In 2022, East Nusa Tenggara (NTT) recorded the highest stunting prevalence in Indonesia at 35.3%. However, quantitative statistical analyses of its contributing factors in NTT remain limited. This study aims to compare partial least squares regression (PLSR) using the NIPALS algorithm with principal component regression (PCR) in addressing multicollinearity. The secondary data were obtained from the 2022 Indonesian Nutrition Status Survey (SSGI), published by the Ministry of Health and BPS NTT, consisting of one response variable and ten predictor variables. Results showed that the PLSR model outperforms PCR, with an adjusted R² of 0.741 compared to 0.322. The superiority of PLSR is also evident from its lower RMSE and MAE values (2.783 and 1.910) compared to PCR (4.742 and 3.346). PLSR identified five significant predictors: average daily protein consumption per capita, number of children receiving DPT and HB immunizations, Human Development Index, percentage of households with access to safe drinking water, and number of people living in poverty.

The Coronavirus disease (Covid-19) has been officially declared a pandemic by the World Health Organization (WHO). This pandemic affects not only the health of the population but also weakens the rupiah exchange rate. Fluctuations in exchange rate changes can affect the investment value, so investors need to take risk measurements. This study discusses the measurement of portfolio loss risk which is composed of a combination of USD, JPY, GBP, and EUR currency exchange rates using the value at risk (VaR) risk measure. Dependent structure analysis was carried out using the Gumbel, Clayton, and Frank copulas approach from the Archimedean copula family. The results obtained from this study are based on portfolio calculations composed of USD-GBP, JPY-GBP, and EUR-USD currency exchange rates at , , and confidence levels in the next one-day period. The highest VaR of is achieved by the EUR-USD portfolio at a confidence level using the Gumbel copula. Meanwhile, the lowest estimated VaR of is achieved by the EUR-USD portfolio at a confidence level using the Gumbel copula.

This study aims to analyze the quality control of chicken egg production at Bram Farm by applying Statistical Quality Control (SQC) tools to identify the level and types of defects that occur during the production process. Quantitative data were obtained from primary documentation through daily records collected over a 14-day observation period, consisting of the total number of eggs produced and the number of defective eggs categorized into specific defect types. Qualitative data were collected through direct observations and unstructured interviews with the farm owner to validate statistical findings and explore operational factors causing defects. The analysis employs three main SQC tools: the check sheet to record defect occurrences, the Pareto diagram to identify dominant defect categories, and the p-chart to evaluate the statistical stability of the production process. The results show that cracked eggs, thin-shelled eggs, and broken eggs are the dominant defects contributing to the overall defect rate. The p-chart indicates that all daily defect proportions fall within the control limits, demonstrating that the production process was statistically stable. These findings suggest that quality control at Bram Farm is effective, although improvement efforts should focus on the dominant defect categories to enhance production quality further.

Traffic accidents are a critical issue in road safety and public health in the United States. This study examines the patterns and statistical stability of annual traffic accidents from 2000 to 2023 using a Statistical Quality Control (SQC) approach. Secondary data were obtained from official publications of the National Highway Traffic Safety Administration (NHTSA), including accident severity, numbers of fatalities and injuries, population size, and risk indicators. The analysis employed five of the seven basic SQC tools, namely check sheets, histograms, Pareto diagrams, control charts (p-charts and u-charts), and cause-and-effect diagrams. The results show that property damage only accidents dominate total crash occurrences, while fatal crashes represent a small proportion but lead to significant loss of life. Control chart analysis indicates statistical instability in the proportion of fatal crashes and fatality rates, suggesting the presence of special causes rather than random variation. Overall, the findings highlight the usefulness of SQC as an effective tool for monitoring traffic safety performance and identifying critical variations requiring targeted interventions.

A graph is a diagram that contains specific information. One concept in graphs that can solve real-life problems is the concept of trees, which consists of various types of trees used to solve problems in life, such as finding the minimum path using the Dijkstra algorithm and the use of minimum spanning trees using the Sollin algorithm. This research produced the minimum path using Dijkstra's Algorithm and the minimum spanning tree using Sollin's Algorithm, which were applied to historical tourist routes in Sumenep Regency.

This study aims to forecast electricity consumption in the Province of Bali using the ARIMA (Autoregressive Integrated Moving Average) method. The forecasting process is based on monthly electricity usage data spanning from January 2015 to June 2024. The initial analysis revealed a significant upward trend, with a notable decline in usage during 2020, coinciding with the COVID-19 pandemic. To address the issue of non-stationarity in the data, a differencing process was applied until stationarity was achieved, as confirmed by the Augmented Dickey-Fuller (ADF) test. Model identification was conducted using ACF and PACF plots, and several ARIMA models were evaluated based on their Akaike Information Criterion (AIC) values. The ARIMA(0,1,1) model was selected as the most suitable model due to its lowest AIC value and its compliance with diagnostic assumptions, including uncorrelated residuals (verified by the Ljung-Box test) and normally distributed residuals (confirmed by the Shapiro-Wilk test). The forecasting results demonstrated that the selected model provides stable predictions for the subsequent 12 months. This study is expected to contribute to effective planning and management of electricity demand in the Bali region.