SMALL AREA ESTIMATION OF THE PERCENTAGE HOUSEHOLDS WITH FOOD EXPENDITURE SHARE MORE THAN 65 PERCENT IN LOW EXPENDITURE GROUP

The right to get adequate food is a human right that must be fulfilled. Food insecurity is a problem that arises from not fulfilling food needs physically or economically. Food insecurity and poverty are interrelated. The United Nations prioritizes the elimination of poverty as the first goal and achieving food security as the second goal in the Sustainable Development Goals. In 2022, East Java had the highest percentage of households with a food expenditure share more than 65 percent in Java. The availability of data by expenditure group illustrates the economic status of households will assist the government in making targeted policies.  However, the calculation of direct estimates at the regency level has not shown good precision, characterized by estimates with an RSE >25 percent. Therefore, this study aims to implement SAE HB Beta to improve the precision of the direct estimator. The result shows that SAE HB Beta produces a more precise estimation

ANALYZING THE LEVEL OF CREDIT FAILURE USING THE AUTOREGRESSIVE DISTRIBUTED LAG TO MAINTAIN STABILITY OF COMMERCIAL BANKS IN MALUKU PROVINCE

Commercial banks are banks that carry out business activities conventionally and or based on sharia principles, which in their activities provide services in payment traffic. The health level of a commercial bank is the result of an assessment of the bank's condition based on risk and bank performance. Commercial Bank performance assessment can use the proxy of asset ownership, namely Return on Assets (ROA). While the risk assessment of commercial banks can use the credit risk proxy used is the Non-Performing Loan (NPL) ratio. The purpose of this study is to examine the Health Level of Commercial Banks in Maluku Province using ROA and NPL based on bank internal factors (bank specific) and macro and micro economic conditions in Maluku Province. The data used is quarterly time series data, namely in the first quarter of 2014 - first quarter of 2022. The method used is multivariate time series data analysis, namely the Autoregressive Distributed Lag (ARDL) model. The results obtained are the Health Level of Commercial Banks in Maluku Province in the first quarter of 2014 - first quarter of 2022 is classified as healthy and stable, even though the Maluku economy is experiencing the impact of the COVID-19 Pandemic. Internal (specific) bank factors are very dominant in influencing the performance and risk of Commercial Banks in Maluku Province compared to macro and micro economic factors. This means that the policies and performance of all parties related to Maluku's economic conditions need to be improved in maintaining the stability and soundness of commercial banks. In general, the performance of all parties in maintaining the health level of Commercial Banks in Maluku Province is very good, especially during the COVID-19 Pandemic

TIME SERIES MODEL FOR TRAIN PASSENGER FORECASTING

Trains as a means of public transportation have an important role in connecting various regions of Jabodetabek. Therefore, it is necessary to have a deep understanding of the trend of train passenger movements and predict the number of train passengers in the next period in order to optimize the management and service of train passengers properly. In this study, we examine two methods that can be used as forecasting methods for train passenger data sourced from the Central Statistics Agency (BPS), namely ARIMA and Prophet. This study demonstrates that the optimal ARIMA model is ARIMA (0,2,1), achieving a Mean Absolute Percentage Error (MAPE) of 4.91% and a Root Mean Square Error (RMSE) of 1754.970. In addition, the Prophet model, which is an additive regression model designed by Facebook for time series forecasting was also obtained with a MAPE of 0.04% and an RMSE of 1170.59. Considering the MAPE and RMSE values of the two models, the Prophet model emerges as the most suitable for forecasting the number of train passengers in the Jabodetabek region

APPLICATION AND PERFORMANCE COMPARISON OF MULTI-OUTPUT MACHINE LEARNING FOR NUMERICAL-NUMERICAL AND NUMERICAL-CATEGORICAL OUTPUTS

Multi-Output Machine Learning is an advancement of traditional machine learning, designed to predict multiple output variables simultaneously while considering the relationships between these output variables. Multi-Output Machine Learning is essential as a decision support tool because decision-making in many problems generally considers multiple factors. The use of Multi-Output Machine Learning is more advantageous than conventional machine learning in terms of time efficiency, addressing data limitations, and ease of maintenance. These benefits will significantly impact cost savings for industries utilizing Big Data. The models used in this research include Multivariate Regression Tree, Multivariate Random Forest, and Multi-Output Neural Network. The Multivariate Regression Tree and Multivariate Random Forest are developed by modifying the splitting function using Mahalanobis distance. The topological changes introducing shared and private hidden layers are the key development of the Multi-Output Neural Network. The prediction results indicated a trade-off in error between two output variables when comparing the Multivariate Regression Tree and Multivariate Random Forest with their single output counterparts. Meanwhile, the Multi-Output Neural Network model successfully improved the prediction results for both output variables. This research also introduces Mixed Multi-Output Machine Learning, which can predict numerical and categorical output variables. The Mixed Multi-Output Machine Learning model utilizes the logit values from the Logistic Regression model to extend the range of prediction results beyond the 0 to 1 interval. Multi-Output Neural Network is the sole model that produces predictions with relatively small errors and high accuracy values

NONPARAMETRIC REGRESSION MODELING USING THE SPLINE APPROACH TO STUNTING CASES IN INDONESIA

Indonesia is the fourth ranked country in the world and second in Southeast Asia with the highest stunting cases of 21.6%. According to the provisions of the World Health Organization (WHO), the maximum tolerance standard for stunted toddlers is 20 percent or one-fifth of the total number of toddlers, so the stunting rate in Indonesia is still relatively high. The high stunting rate in Indonesia can affect the quality of Indonesia's human resources, so early detection and immediate management of stunted toddlers are needed. Stunting is a condition of failure to grow due to chronic malnutrition which is caused by inadequate nutritional intake for a long time, resulting in being shorter than standard. This research aims to determine several factors that influence stunting in toddlers in Indonesia using the nonparametric spline regression method with one knot, two knots, three knots and the best model is found to be the one knot model. The results of regression nonparametric spline modeling with one knot are GCV of 14.32605 and  of 81.1%. From the five variables, namely toddlers receiving complete basic immunization  babies receiving exclusive breast milk for 6 months , babies born receiving IMD  children aged 6-23 months consuming five of the eight food groups and drink throughout the day , households having access to proper sanitation , the following results were obtained: the variable that don’t have a significant effect was toddlers receiving complete basic immunization , while the other four has a significant effect

Wolbachia Pipientis as a Natural Biocontrol Agent to Inhibit Dengue Virus Replication in Aedes Aegypti Mosquitoes

Wolbachia pipientis is an endosymbiotic bacterium commonly found in insects, arthropods, and certain nematodes, playing a pivotal role in host reproductive manipulation and pathogen resistance enhancement. This review aims to provide a comprehensive understanding of the characteristics, physiology, and ecological roles of Wolbachia pipientis, as well as its potential in vector control strategies to combat insect-borne diseases. A literature-based approach was employed, utilizing peer-reviewed scientific journals from reputable sources such as Scopus and Web of Science. The analysis revealed that Wolbachia pipientis is a Gram-negative bacterium with a small size (0.2 to 0.5 µm) that reproduces through binary fission within host cells and relies on the host for essential nutrients. It modulates host gene expression and enhances host immunity, thereby reducing the transmission of pathogens like dengue, Zika, and chikungunya through cytoplasmic incompatibility. Furthermore, Wolbachia pipientis has shown potential in managing filariasis by inhibiting nematode reproduction. The bacterium's ability to alter host reproductive dynamics through mechanisms like feminization and parthenogenesis makes it an effective tool in biocontrol programs. The findings underscore the importance of Wolbachia pipientis in improving public health and environmental sustainability by controlling disease vectors and supporting ecological balance. This review highlights the need for further research to explore genetic engineering approaches to enhance the effectiveness of Wolbachia pipientis in global health management

DYNAMIC ANALYSIS OF PREDATOR-PREY MODEL WITH CANNIBALISM INTERVENTION AND DISEASE INFECTION IN PREY USING HOLLING TYPE II RESPONSE FUNCTION

This study discusses the intervention of cannibalism and disease spread with Holling Type II response function in the predator-prey model. It is assumed that disease infection is limited to the prey population and cannot be cured so that in this model there are three subpopulations namely susceptible prey, infected prey and predators. In addition, there is cannibalism in the predator population. The objectives of this study include constructing a predator-prey model with cannibalism intervention and disease infection in prey using Holling Type II response function, identifying the stability of the equilibrium point of the model and interpreting the model based on simulation results. Analysis of the stability of the equilibrium point is carried out with a linearization approach and the Routh-Hurwitz criterion was used to determine equilibrium stability. Based on the stability analysis, 5 (five) equilibrium points are obtained, namely population extinction, susceptible prey exists, predator extinction, infected prey extinction and population exists where the population extinction equilibrium point is unstable and the other equilibrium points are stable with the certain conditions. From the simulation, it is obtained that the numerical results are in accordance with the analytical results of the stability analysis of the equilibrium point of the model and for infinite time, there will be no population extinction while the state of susceptible prey exists, predator extinction, infected prey extinction and population exists can occur if the stability conditions are met. Based on the numerical simulations, it was found that changes in the parameter values of the rate of change of susceptible prey to infected prey and the coefficient of predator cannibalism in day-1 can cause changes in the type of stability of the equilibrium point. Thus, rate of change susceptible prey to infected prey and the coefficient of predator cannibalism affects the population of prey and predator

IMPLEMENTATION OF THE DBSCAN ALGORITHM FOR CLUSTERING STUNTING PREVALENCE TYPOLOGY IN WEST JAVA, CENTRAL JAVA, AND EAST JAVA REGIONS

Stunting, a condition where children are malnourished for a long period, causes growth failure in children. West Java, Central Java, and East Java are the 3 provinces with the highest prevalence of stunting in 2021. This study aims to group districts/cities in these provinces based on factors that influence stunting using the DBSCAN method (there has been no previous research using this method for this case), so the typology of stunting prevalence is implied. The group results can be valuable input for policy priorities in overcoming stunting. The study used the DBSCAN (Density-Based Spatial Clustering of Application with Noise) method, which can also detect noises (outliers). The determination of eps and MinPts is based on the average value of the distance from each data to its closest neighbor. The distance obtained then was used in the KNN algorithm to determine eps and MinPts parameters. Clustering is done using standardized data and DBSCAN parameters obtained from the k-dist plot, eps is 1.92, and MinPts is 2. The validation test used is the silhouette coefficient to determine the goodness of the cluster results. The clustering results show that there are 2 clusters and 1 noise that have special characteristics related to factors that influence the prevalence of stunting. Cluster 1 consisted of 97 districts/cities and was characterized by a high percentage of infants under 6 months receiving exclusive breastfeeding and the lowest average per capita household expenditure. Cluster 2 (Bekasi City and Depok City) was characterized by the lowest percentage of households with proper health facilities and infants aged 0-59 months receiving complete immunization. The noise (high stunting prevalence) in Bandung City is characterized by the lowest percentage of households having proper sanitation

SEIR MODELING OF TUBERCULOSIS TRANSMISSION WITH VACCINATION: ESTIMATING THE MINIMUM COVERAGE REQUIRED FOR ELIMINATION IN NORTH SUMATERA

Tuberculosis (TB) remains one of the most significant public health challenges in Indonesia, particularly in North Sumatra Province, which records a relatively high prevalence rate. e–Exposed–Infectious–Recovered) A framework to analyze TB's transmission dynamics and evaluate vaccination's effectiveness as an intervention strategy. The primary objective of this research is to estimate the minimum vaccination coverage required to eliminate TB from the population. The model was built by incorporating vaccination rate parameters into the SEIR mathematical model and applied using regional epidemiological data obtained from the North Sumatra Central Bureau of Statistics. A deterministic approach is employed to simulate the system and derive the basic reproduction number ( ), which serves as an indicator of disease persistence across various levels of vaccination coverage. The results of numerical simulations performed using 4th-Order Runge Kutta indicate the existence of a critical vaccination threshold required to reduce  below the one-condition that theoretically represents the possibility of disease elimination from the population. These findings provide a quantitative basis for formulating more targeted, data-driven vaccination policies. Calculations based on real-world data reveal that the current R₀ value in North Sumatra remains above one ( ), suggesting that TB continues to pose a risk of remaining endemic. Simulations were also conducted by varying the vaccination coverage while assuming a constant transmission rate, indicating that a minimum of 87.5% vaccination coverage is required to suppress R₀ below the critical threshold. This study underscores the importance of employing mathematical modeling as a decision-support tool in public health policy. The findings deepen the understanding of TB transmission dynamics and offer a robust quantitative foundation for setting vaccination targets for disease elimination in endemic areas such as North Sumatra. &nbsp

THE TRIPLE IDENTITY GRAPH OF THE RING Z_n

Let  be a commutative ring with identity and  is an identity element of . The triple identity graph of the ring , represented by ), is an undirected simple graph with the vertex set . In , two different vertices  and  is called adjacent if there is an element such that and . The triple identity graph of the ring of integers modulo , represented by , is the subject of this study. We obtain several results regarding the properties of the graph , which are summarized as follows. The graph  is a connected graph if and only if  is prime and . If  is connected, then diam and gr. Furthermore,  is a Hamiltonian graph if  is a prime number and