JURNAL ILMIAH MATEMATIKA DAN TERAPAN

Location Based Stunting Modeling Using Geographically Weighted Panel Regression in Blitar Regency

Henny Pramoedyo — 2024-12-16

Stunting remains a significant public health issue in Blitar Regency, Indonesia, particularly in rural areas where chronic malnutrition and inadequate access to healthcare services persist as major challenges. This study aims to explore the spatial and temporal factors influencing stunting using the Geographically Weighted Panel Regression (GWPR) method. By integrating cross-sectional and time-series data from 2021 to 2023, the study evaluates various factors, including the stunting prevalence rate and independent variables such as maternal education level, per capita income, the number of postpartum mothers receiving Vitamin A supplements, immunization coverage, and the availability of healthcare personnel. The findings reveal that stunting prevalence is significantly influenced by location-specific variables, with healthcare access and nutrition being dominant factors in rural areas, while economic conditions exert a greater influence in urban areas. The GWPR model provides deeper insights into spatial heterogeneity and offers valuable guidance for designing targeted and region-specific policies to reduce stunting rates in Blitar Regency. The results indicate that the R-Square value of the GWPR model is 0.9123, meaning that 91.23% of the stunting prevalence in Blitar Regency can be explained by the independent variables in this study

Biplot Analysis for Spatial Mapping of Dengue Hemorrhagic Fever (DHF) Incidence in Indonesia

Fadjryani Abdul Gani — 2024-12-16

Dengue Hemorrhagic Fever (DHF) is a serious threat to Indonesian public health, with the dengue virus spread by the Aedes aegypti mosquito continuing to claim victims in all provinces in Indonesia. The drastic variation of DHF incidence between provinces requires an in-depth understanding of its distribution pattern. Biplot analysis allows researchers to identify patterns based on factors that influence the incidence of DHF in different provinces. This study aims to identify the spatial distribution pattern of DHF in Indonesia using biplot analysis, an approach that allows complex visualization of factors affecting DHF incidence. Results showed that 62.48% of the data variation could be explained through biplot representation, revealing spatial distribution patterns, proximity between objects and diversity between variables. Key findings include the identification of provinces with the highest DHF cases (56,388 cases) in quadrant IV, the high incidence of DHF cases was associated with similar characteristics of average air humidity. In addition, there was significant variation in the number of DHF cases between provinces indicating disparities in the number of DHF cases in different parts of Indonesia, as well as relative uniformity in the percentage of households with proper sanitation (descriptive average of 86.62%). The results of this study are expected to assist policy makers in formulating more effective and targeted dengue prevention and control strategies, potentially reducing the incidence of dengue and improving the health of the Indonesian people.

Optimization of Agricultural Land with the Hungarian Algorithm Method (Case Study: Agricultural Land in Tuatuka Village, Kupang Regency)

Vera Selviana Adoe — 2024-12-16

This research delves into how the Hungarian Algorithm's utilized to streamline workforce allocation, for activities in Tuatuka Village located in Kupang Regency in East Nusa Tenggara region of Indonesia. The primary obstacles encountered include shortage of labor and differences, in skill levels that affect productivity levels significantly. By employing the Hungarian Algorithm a mathematical method is utilized to reduce costs and time associated with assigning tasks effectively by matching workers to duties based on their skills and capabilities. This study includes gathering data by observing and conducting interviews that are later examined using POM QM, for Windows V5 software and manual computations. The results indicate that implementing this method can cut down total project expenses to 35 work hours through task allocation strategies. The adoption of the Hungarian Algorithm has been successful in improving workforce efficiency in agriculture areas leading to output. Decreased resource wastage. Therefore this study aids, in streamlining operations in the industry especially when it comes to managing resources in rural settings.

Optimization of Overdispersion Modeling in Low Birth Weight Cases in Central Sulawesi Using Conway-Maxwell Poisson Regression

Nurul Fiskia Gamayanti — 2024-12-16

Low birth weight (LBW) is a condition of a baby weighing less than 2,500 grams where gestational age is not taken into account and the baby's weight is measured within 24 hours after birth. The level of infant development also plays an important role in determining the mortality rate and incidence rate of disease in infants with LBW. This study aims to find models and factors that influence LBW using Conway Maxwell Poisson Regression (CMPR). CMPR is an extension method of Poisson regression that has the advantage of overcoming violations of the equidispersion assumption, where data can experience overdispersion or underdispersion

A GARCH Model for Forecasting Volatility of Oil and Gas Exports in Indonesia

Umi Mahmudah — 2024-12-16

This research aims to analyze the dynamics of oil and gas exports in Indonesia during the period from 2012 to 2022 using the GARCH approach for forecasting volatility. The data utilized in this study encompass the monthly published volumes of Indonesia's oil and gas exports, sourced from the Indonesian Central Statistics Agency's website. The analysis involves a substantial amount of data, comprising 132 monthly time series spanning a significant timeframe. The findings indicate that the most suitable model for predicting oil and gas volumes is the GARCH (1,1) model. The GARCH approach is employed to model the volatility within the data of oil and gas exports. The results reveal the utilization of information criteria, including Akaike (14.73), Bayes (14.86), Shibata (14.73), and Hannan-Quinn (14.79). Moreover, the forecast analysis for the next ten periods depicts a consistent upward trend. Generally, these forecast results suggest that while the mean values of the data remain relatively stable, the volatility levels are anticipated to increase over the forthcoming periods. The implications of this research are crucial within the context of economic and international trade, as the volatility in oil and gas exports can significantly impact national economic policies and corporate decisions.

Grouping of Regencies/Cities in Indonesia Based on National Health Insurance (JKN) Participants with the Ensemble ROCK Approach

Rahmania Azwarini — 2024-12-16

Health is a fundamental human need, and the National Health Insurance (JKN) program was established in Indonesia to provide equitable access to healthcare services for all citizens. Despite its implementation, disparities remain across regencies/cities, necessitating a comprehensive mapping of JKN participant profiles. This study aims to group 34 regencies/cities in Indonesia based on the characteristics of JKN participants, utilizing numerical and categorical data clustering. The Ensemble Robust Clustering using links (ROCK) method was employed, combining hierarchical clustering for numerical data and the ROCK method for categorical data. The study analyzed data comprising eight numerical variables (age, household size, household total expend, expend healthcare, tobacco expend, ATP, WTP, and expend insurance) and six categorical variables (living area, sex, education, reasons for joining JKN, ATP, WTP). Numerical clustering through single linkage yielded four clusters, while categorical clustering with the ROCK method at a threshold value of 0.2 produced three groups. The final ROCK ensemble analysis integrated these results, forming three quality-based clusters: low, medium, and high. Key findings revealed distinct socio-economic and demographic patterns among the clusters. For instance, the low-quality group exhibited lower household expenditures and healthcare spending, while the high-quality group had higher averages across these variables. Insights from this study can guide policy-makers in prioritizing healthcare resources and addressing regional disparities in JKN implementation.

Determination of Motor Vehicle Insurance Risk Premium

Pramesti Melyna Mustofa — 2024-12-16

Insurance is a service that transfers specific financial loss risks to an insurer in exchange for a fixed payment, known as a premium. The determination of this premium is tailored to the policyholder's level of risk. In this study, the calculation of premium risks is conducted by analyzing the frequency and size of claims related to motor vehicle insurance. The analysis focuses on different types of vehicles and their associated risks, as well as variations in vehicle usage based on geographical regions. This approach enables insurers to better understand risk patterns and predict potential future losses, ensuring accurate premium determination

Temperature Data Prediction in South Sulawesi Province Using Seasonal-Generalized Space Time Autoregressive (S-GSTAR) Model

Muhammad Edy Rizal — 2024-12-16

Indonesia's distinct tropical climate is influenced by its geographic location near the equator and its complex topography, resulting in pronounced seasonal temperature patterns. This study examines the application of the Seasonal Generalized Space-Time Autoregressive (SGSTAR) model to forecast the average air temperature in four regions of South Sulawesi Province: North Luwu, Tana Toraja, Maros, and Makassar. The dataset comprises monthly average temperatures from January 2019 to October 2024, sourced from BMKG's online database. The analysis includes stationarity testing using the Augmented Dickey-Fuller (ADF) test, seasonal pattern identification with autocorrelation function (ACF), and formal seasonal tests such as QS, QS-R, and KW-R. Spatial weight matrices were constructed based on Euclidean distances between regions. The best model was selected based on Mean Square Error (MSE), Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), and adjusted R² criteria. The findings reveal that the seasonal GSTAR model with AR orders (p=3), (ps=4), and (s=12) is the optimal model. Evaluation indicates that the model achieves high accuracy, with forecast errors (MSE and RMSE) below 1°C. This model effectively captures seasonal and spatio-temporal patterns in climate data. The study is expected to serve as a foundation for further development of seasonal GSTAR models for other climate datasets, supporting improved environmental planning and resource management.

Comparison of Random Survival Forest and Fuzzy Random Survival Forest Models in Telecommunications Industry Customer Data

Sitti Nurhaliza — 2024-12-16

The telecommunications sector is facing increasing competition, and customer churn is still a major
challenge despite the implementation of advanced promotions and high-quality services. Churn refers to
the discontinuation of services by customers, influenced by several factors that can be found through data
modeling. This study compares two predictive models, Random Survival Forest (RSF) and Fuzzy Random
Survival Forest (FRSF), for predicting customer churn time in the telecommunications industry. Both
models are evaluated using the median C-index value obtained from 20 repetitions, ensuring more
consistent and reliable results. RSF, a widely used survival analysis method, has shown strong predictive
power, with studies reporting up to 99% accuracy in churn prediction. However, FRSF, a modified version
that incorporates fuzzy logic, has proved superior performance, particularly in handling imprecise or
uncertain data. The results show that FRSF achieves a lower error rate of 0.1739, compared to RSF's error
rate of 0.1906. These findings suggest that FRSF outperforms RSF in churn prediction, making it a more
reliable and righter model for finding at-risk customers. The study concludes that the FRSF model is the
preferred choice for predicting churn in the telecommunications industry, offering better predictive quality
and consistency in handling uncertain data.

Analysis of Predicting the Exchange Rate of the IDR Against the US Dollar Using the Fuzzy Time Series Methods of Chen and Cheng

Aden Aden — 2024-12-16

The rise and teh exchange rate of the IDR against the US dollar fall starting from 2021 to 2023 will have many impacts in the Indonesian economy both positive and negative impacts. The resulting impact affects the sustainability of the country's economic activities. To maintain the stability of the IDR exchange rate against the US dollar, it is necessary to do forecasting so that it can monitor the movement of the IDR exchange rate in the future. The puIDRose of this study is to find out how the results of the prediction of the IDR Exchange Rate (exchange) against the US Dollar with the Fuzzy Time Series Chen and Fuzzy Time Series Cheng methods and how the comparison of the implementation of the Fuzzy Time Series Chen and Fuzzy Time Series Cheng in predicting the IDR Exchange Rate (exchange) against US Dollar based on MAPE Value. The method used in this research is Chen and Cheng's Fuzzy Time Series. Forecasting results indicate the IDR exchange rate against the US dollar in June and July 2023 in the application of Chen's Fuzzy Time Series method of IDR 15,150.32 and the Fuzzy Time Series Cheng method of IDR 15,127.77. Then the data analysis shows that the MAPE results from the FTS Cheng method are better than the FTS Chen method with a MAPE percentage of 0.970%. The results of the acquisition of this percentage can be used as a reference for the government to weigh the economic policies that will be enacted in order to reduce the negative impacts arising from fluctuations in the IDR exchange rate.