Limits: Journal of Mathematics and Its Applications

Pemodelan Angka Harapan Hidup Negara G7 dengan Pendekatan Analisis Regresi Data Longitudinal

Wed, 26 Mar 2025 00:00:00 +0000

Life expectancy is the average number of years of life a newborn baby will live in a given year. In general, life expectancy is a tool to evaluate government performance in improving community welfare. The aim of this research is prediction using longitudinal data regression analysis methods, namely Generalized Least Square with a Restricted Maximum Likelihood approach using a uniform correlation structure, Autoregressive (AR) (1), and Gaussian with factors that influence life expectancy, namely Tax to GDP ratio, Gross Domestic Product per Capita (GDPPC) and Health Expenditure per Capita from 2000-2020 in G7 countries. Based on the analysis results, it was found that tax revenues had a negative effect of 0.155 but the effect was not significant, GDP had a positive effect of 0.715 but had a significant effect, while health expenditure had a negative effect of 0.49 on Life Expectancy. The research results found that conditions in the G7 that were not ideal caused negative effects on taxes and health spending that were not in accordance with theory. The suggestions that can be given include tax reform from the source and its implementation, such as cigarette tax and sugary drink tax. In addition, it also provides suggestions to include universal health for a healthier and more prosperous society. This research is also in accordance with the aim of Sustainable Development Goals (SDGs) number 3, namely "Ensuring healthy lives and improving the welfare of all populations of all ages" and can be used as a policy reference for Indonesia.

Property Crime in Java Island 2022 based on Demography and Socioeconomic Aspects using Spatial Analysis Approach

Fabian La Wima Vallessy, Timbang Sirait — Wed, 26 Mar 2025 00:00:00 +0000

Property crime is the most common type of crime in Indonesia with the most rapid increasing in 2022. Java is the island with the highest magnitude of 65.85% if it is compared to the previous year and accounts for more than one third of the total cases in Indonesia. This study aims to determine an overview of these types of criminal offenses and the variables that affect them spatially. The analysis method uses in this study is descriptive analysis which will followed by inferential analysis, namely spatial analysis using Geographically Weighted Negative Binomial Regression (GWNBR). Based on this research, it is found that there are four regional groupings with variables that significantly affect all regions, namely life expectancy and Gini ratio. Meanwhile, there are variables that affect some regions, namely mean years schooling and total population. In addition, it is found that Geographically Weighted Negative Binomial Regression is better used than negative binomial regression in modeling property crime in Java Island in 2022.

Variables that Influence Urban Sprawl in DKI Jakarta, West Java and Banten Provinces in 2020

Azzahra Dhisa Khamila, Timbang Sirait — Wed, 26 Mar 2025 00:00:00 +0000

DKI Jakarta, West Java and Banten provinces are the place of two large metropolitan areas in Indonesia that are interconnected. As a result, these areas have a high level of urbanization which can lead to urban sprawl. Urban Sprawl can cause various negative impacts, especially on the environment. Therefore, it is necessary to minimize urban sprawl, one of many ways is by analyzing the variables that affect urban sprawl. Several studies on spatial analysis of urban sprawl have been made extensively using satellite imagery data, one of them states that NDBI can capture patterns, characteristics and the causes of urban sprawl. However, research that utilizes NDBI as a variable approach for the urban sprawl has never been conducted in Indonesia. Therefore, this research was conducted with the aim of analyzing the effect of variables that indicated influence urban sprawl in the provinces of DKI Jakarta, West Java and Banten using spatial analysis. The results show that the average NDBI value is high in urban areas where the majority are in DKI Jakarta province. The variables that significantly influence urban sprawl are percentage of migrant population and tertiary sector of GRDP. By focusing on these variables, the government can make policies to minimize and control urban sprawl that occur in their area.

Analisis Survival Distribusi Lomax dengan Estimasi Maximum Likelihood

Wed, 26 Mar 2025 00:00:00 +0000

Survival analysis is a statistical technique used to test the durability and reliability of a component. Life time data obtained from a life test experiment is often in the form of type III censored data, which occurs when observations enter at different times and last for varying durations. In survival analysis, data is expected to follow a certain probability distribution. To determine the characteristics of a population, a point estimate of the probability distribution parameters is conducted. This study aims to obtain parameter estimators of the Lomax distribution on type III censored data with the Maximum Likelihood Estimation (MLE) and Newton Raphson methods. Application of parameter estimation results on post-heart surgery survival data in one of the Jakarta hospitals. The result of estimating the parameter value in the post-heart surgery patient data is 1.552 and the result of estimating the parameter in the post-heart surgery patient data is 20.38. Based on these results, it can be concluded that the estimated probability of survival of a post-heart surgery patient for more than 49 days is 14.94%.

Perbandingan Metode Regresi Ridge dan Jackknife Ridge Regression pada Data Tingkat Pengangguran Terbuka

Agita Andini, Etis Sunandi, Pepi Novianti, Idhia Sriliana, Winalia Agwil — Wed, 26 Mar 2025 00:00:00 +0000

Regression analysis is a statistical technique used to analyze the relationship between predictor and response variables. One of the parameter estimation methods commonly used for regression analysis is Ordinary Least Squares. This method produces unbiased and efficient estimates, known as BLUE (Best Linear Unbiased Estimator). In multiple linear regression analysis involving more than one predictor variable, it is essential to meet model assumptions such as the absence of multicollinearity. Multicollinearity is a condition where predictor variables have a high correlation, which can disrupt the stability of parameter estimates. Therefore, Ridge Regression and Jackknife Ridge Regression methods were used to address this issue. Both methods modify the least squares method by adding a bias constant value. This research uses the Open Unemployment Rate (OUR) data in Sumatra in 2022, and 3 predictor variables exhibit multicollinearity. Based on the analysis comparing the Mean Squared Error (MSE) values, the Jackknife Ridge Regression method yields the smallest MSE value, 0.004. Both methods are effective in addressing multicollinearity and identifying significant predictor variables for OUR in Sumatra Island, namely the Human Development Index (HDI), average years of schooling, number of poor people, Life Expectancy (LE), population density and inactive population

Model Regresi Gamma untuk Menganalisis Indeks Pengeluaran Kabupaten/Kota di Pulau Sumatra

Bambang Widjanarko Otok, Dyah Setyo Rini, Rahmi Fadhilah — Wed, 26 Mar 2025 00:00:00 +0000

Gamma regression is part of Generalised Linear Models (GLMs) that can model data that is positive and asymmetric. The occurrence of data asymmetry is common in everyday life, for example in Human Development Index (HDI) data. The HDI has indicators called the Human Development Dimension Index, including the expenditure index, the education index and the life expectancy index. This study aims to model the expenditure index of districts/cities in Sumatra using Gamma regression because the expenditure index data is positive and non-symmetric. In modelling the Expenditure Index, the predictor variables used are the percentage of poor population, population density, percentage of population using their own toilet, and open unemployment rate in each district/city in Sumatra in 2023. The data used were obtained from the BPS website of the province corresponding to the regency/city in Sumatra. Based on the results of the analysis, all the predictor variables used had a significant effect on the expenditure index at the 1% and 5% significance levels, and the standard error value of each parameter estimate was small. In addition, the MSE of the model is also classified as small, which is 0.00163. This can prove that the model is supported by the data, although the coefficient of determination of the model ( ) in this study is only 47.59%.

Pembentukkan Tabel Morbiditas Penyakit Kronis Berdasarkan Angka Prevalensi

Khairul Alim, Gusmi Kholijah, Meinarisa Meinarisa — Wed, 26 Mar 2025 00:00:00 +0000

A morbidity table is an important mathematical tool in health sciences and actuarial studies, providing an overview of disease rates in a population at a given time. This table offers information on the number of disease cases by age group, type of disease, or geographical region. The values from morbidity tables are used to compare disease rates between populations, evaluate the effectiveness of health programs, and predict future healthcare service needs.

In the world of health insurance, morbidity tables play a crucial role in premium pricing calculations. By using specific morbidity tables, insurance providers can set premium prices that align with the conditions of the population being covered, preventing company losses due to premiums being set too low, while also ensuring that people do not feel burdened by excessively high premiums.

Despite their importance, creating morbidity tables requires extensive research and large amounts of data. In Indonesia, accurately constructing morbidity tables is challenging due to its vast geographical diversity. Therefore, this study will use the prevalence rate of chronic diseases as an alternative. Prevalence rate is a statistical measure that describes the proportion of individuals in a population suffering from a specific disease at a given time. This approach simplifies data collection, especially since the government regularly releases prevalence data for certain diseases.

Pengaruh Suplementasi Vitamin D dan BMI terhadap LVEF dengan Pendekatan Generalized Additive Models Longitudinal

Wed, 26 Mar 2025 00:00:00 +0000

Cardiovascular diseases (CVD) are the leading cause of global mortality, with Left Ventricular Ejection Fraction (LVEF) being a key indicator of heart function. This study explores the impact of vitamin D supplementation and Body Mass Index (BMI) on LVEF using Generalized Additive Models (GAM) in longitudinal data from 47 elderly patients with hypovitaminosis D undergoing orthopedic surgery. LVEF was measured before surgery and at 1, 3, and 6 months post-intervention. GAM was employed to capture nonlinear relationships between variables with working correlation structures such as Independence, Exchangeable, Unstructured, and Autoregressive-1 (AR-1). The findings revealed a significant increase in vitamin D levels and LVEF following supplementation, while BMI remained relatively stable throughout the observation period. The best GAM model with AR-1 correlation structure achieved the lowest Quasi Information Criterion (QIC) score of 443.47, indicating a complex relationship between vitamin D and LVEF and a linear relationship between BMI and LVEF. Vitamin D demonstrated a significant nonlinear effect on LVEF improvement, whereas a 1-point increase in BMI raised LVEF by 0.291%. This study underscores the importance of vitamin D supplementation in enhancing heart function among elderly patients with hypovitaminosis D, supporting the development of evidence-based health policies

Prediksi Harga Saham Big Four Banks di Indonesia Menggunakan Deret Fourier Multirespon

Mochamad Rasyid, Sediono Sediono, M. Fariz Fadillah Mardianto, Elly Pusporani — Wed, 26 Mar 2025 00:00:00 +0000

Perbandingan Metode GARCH, LSTM, GRU, dan CNN pada Peramalan Volatilitas Kurs

Adeline Vinda Septiani, Farit Mochamad Afendi, Anang Kurnia — Wed, 26 Mar 2025 00:00:00 +0000

Currency volatility is an important aspect of time series data analysis in economics and finance. This study aims to compare the performance of four methods: Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN), in predicting the volatility of the Rupiah against the US Dollar. The data used is daily exchange rates from January 2015 to March 2024. The evaluation is conducted by calculating the Root Mean Square Error (RMSE) and the percentage of actual values within a 95% confidence interval on training and testing data. The results indicate that LSTM achieves the lowest RMSE, with values of 5.30E-05 on training data and 2.50E-05 on testing data, demonstrating high accuracy in capturing non-linear patterns and long-term fluctuations. GRU records the highest percentage of actual values within the confidence interval, at 90.32% for training data and 91.72% for testing data, reflecting superior consistency compared to other methods. Meanwhile, GARCH shows competitive performance but lacks robustness on testing data. CNN exhibits the lowest performance, with high RMSE and a low percentage of data within the confidence interval. Overall, GRU emerges as the best method, offering an optimal balance between predictive accuracy and consistency, making it a reliable tool for modeling exchange rate volatility in high-volatility scenarios. Consequently, GRU is utilized for forecasting exchange rate volatility for the next 30 days. These findings contribute to the selection of appropriate methods for modeling exchange rate volatility, particularly amidst global market uncertainty.