Percentage Of Poor Population Research Articles

MODELING CHRONIC FILARIASIS CASES IN WEST JAVA USING A MULTIVARIATE ADAPTIVE APPROACH REGRESSION SPLINES

One of the most crippling infectious diseases in the world is filariasis. Indonesia is a unitary country with 34 provinces, where West Java is one of the 5 provinces with the most filariasis sufferers in Indonesia as of 2021. Reinfection occurs in places that have implemented POMP. Therefore, monitoring operations must be carried out to track the emergence of new cases and risk factors for transmission. The aim of this research focuses on describing and modeling the number of chronic filariasis in West Java, as well as interpreting the best model results obtained. The method used is a method with a nonparametric regression approach, namely Multivariate Adaptive Regression Spline. The results of the research show that the best model obtained is a combination of 15 base functions, maximum interaction 2, and minimum observation between knots 1. From this model, the predictor variable that has the most influence on the response variable in order based on the level of variable importance is the Percentage of Population Access to Facilities Decent Sanitation, Percentage of Households with Clean and Healthy Behavior (PHBS), Sex Ratio, and Percentage of Poor Population. The interpretation of the best model is that the variable Percentage of Population Access to Adequate Sanitation Facilities above 6,650% will contribute to a reduction in the number of chronic filariasis; the Sex Ratio variable below 103,300 will contribute in the form of a reduction in the number of chronic filariasis. it can be seen that the predictor variable that has the most influence on the response variable is the variable Percentage of Population Access to Proper Sanitation Facilities with an importance level of 100%.

Children's Resilience to Not Working Before and During the Pandemic in Rural Indonesia

Children are an asset to a country and a resource that will further national and international development goals. Children who work will be threatened by their health, safety, education, and development. In the last 10 years, BPS has recorded the percentage of child labor fluctuating with the latest record in 2021 at 2.63 percent. The high percentage makes the phenomenon of child labor still a global concern to be addressed as contained in the Sustainable Development Goals. Therefore, the purpose of this study is to determine the determinants of children's resilience not to work in rural areas during the observation period before and during the COVID-19 pandemic. This study uses a three-level survival analysis method by utilizing Sakernas data for August 2019-2020 and data from the BPS website. The results of this study obtained that the percentage of children aged 5-17 years who worked increased during the pandemic by 5.62 percent to 18.88 percent in rural areas. The variables of child gender, child education, child status at individual level; household head gender, household head occupation sector, and household poverty status at household level; and percentage of poor population at districts level significantly affect the resilience of children aged 5-17 not to work in the period before and during the COVID-19 pandemic.

GEOGRAPHICALLY WEIGHTED RIDGE REGRESSION MODELING AT THE OPEN UNEMPLOYMENT RATE IN WEST KALIMANTAN

Geographically Weighted Ridge Regression (GWRR) is a development of ridge regression by adding a weighting element as additional information. GWRR is used to overcome spatial data containing local multicollinearity by modifying the ridge regression and GWR. The purpose of this study was to construct the GWRR model and analyze the effectiveness of the GWRR model in overcoming multicollinearity problems in spatial data. The research data used consists of response variables, namely the value of the open unemployment rate $(Y)$ in districts/cities of West Kalimantan Province in 2021 and the explanatory variables used are district/city minimum wage $(X_1)$, population $(X_2)$, percentage of poor population $(X_3)$, labor force $(X_4)$, and average years of schooling $(X_5)$. The analysis process was first carried out by using multiple linear regression modeling and GWR modeling. After that, centering and scaling transformations were carried out on the data, followed by GWRR modeling to overcome the problem of multicollinearity in spatial data. After all the models were obtained, a model comparison was made in terms of the MSE value. Based on the results of the study, 14 GWRR models were obtained by dividing into three regional groups based on the factors that affect the open unemployment rate for each district/city in West Kalimantan Province. In the comparison of the models used, when viewed from the MSE value, the GWRR model has the highest MSE value compared to the linear regression model and the GWR model. Even though the MSE value in the GWRR model is the largest among the three models, in the GWRR model the problem of multicollinearity can be resolved.

COMPARISON OF K-MEANS AND GAUSSIAN MIXTURE MODEL IN PROFILING AREAS BY POVERTY INDICATORS

The Covid-19 pandemic has led to income degradation of the Indonesia population which potentially triggers poverty. According to the Indonesian Central Statistics Agency, the Province of Central Java is one of the areas that is most affected by Covid-19 especially on the economic aspect. In 2020, the percentage of poor people has increased by 0.6% from 2019. If this condition is ignored for the long term, it will have a negative impact on hampering national development. As a first step in designing a strategy for mitigating the impact of poverty, it is necessary to carry out an appropriate profiling of the areas affected on the economic aspect based on poverty indicators. This study compares the K-Means Clustering and Gaussian Mixture Model (GMM) in providing the best data grouping based on clustering indexes, including: connectivity, Dunn, and silhouette. GMM is a generalization of K-Means clustering to include information about the covariance structure of the data as well as latent Gaussian centers. We used poverty indicators data from Central Statistics Agency of Central Java, such as poverty line, percentage of poor population, poverty depth index, and poverty severity index. The results obtained from this study indicate that the GMM gives the best results with the 3 clusters, with the number of members for the first, second, third is 10, 19, and 6 respectively.

Modeling Human Development Index in Papua and West Sumatera with Multivariate Adaptive Regression Spline

The Human Development Index (HDI), which is an indicator as a comprehensive description of the measure of achievment development in the quality of human life in a region. In Indonesia, there are still many areas with low HDI, especially in Papua Province. This study aims to model and find out what factors affect HDI in Papua Province and West Sumatera Province, using Multivariate Adaptive Regression Spline (MARS). MARS is a modeling methods that can handle high-dimensional data, namely data that has independent variables and a sampel size of data with unknown data patterns in advance, and can be applied to see interaction between the variables used. The result of this study obtained that the best MARS model for Papua Province is a combination of BF=24, MI=2, and MO=0 with GCV=0.55953. while the best MARS model for West Sumatera Province ia a combination of BF=24, Mi=2, and MO=0 with GCV=0.02697. the factors that significantly affect HDI in Papua Province and West Sumatera Province are average lenght of schooling (X2), adjusted per-capita income (X6), life expectancy (X1), percentage of poor population (X4), anf gross regional domestic product (X3). The level of importance of each variable for Papua Province is 100%, 45.26%, 29.24%, 6.55%, and 6.27%. while for West Sumatera Province it is 100%, 96.73%, 57.54%, 34.13%, and 29.6%,respectively. So that in this case based on research results the average lenght schooling (X2) is the variable that most influences HDI in the two regions.

Analisis Penyebab, Konsekuensi, Dan Solusi Potret Kemiskinan Di Kabupaten Mojokerto Pasca Reformasi

This study analyzes the causes, consequences and solutions to portrait poverty in post-reform Mojokerto Regency, including: Poverty Line, Number of Poor Population, Percentage of Poor Population, Consequences and Solutions to Post-Reform Poverty. Poverty is a very dynamic problem that depends on the challenges faced. In Indonesia, each government period has its own focus and character in efforts to reduce poverty. Preparing for the analysis requires information to be used in the research process. Information in the form of research published by the Central Bureau of Statistics of Finland (BPS), other information from the BPS of East Java Province, BPS of Mojokerto Regency, using time series data (time series data) at least from the most recent period of 3-5 years, and using djpb data from the Indonesian Ministry of Finance. The results of the study show that the poverty line is known to be directly proportional to the increase in the number of poor people in 2020. This indicates that the increase in the price of food and other staple goods will be far above the average. spending of the poor. The very poor and the poor remained in place, while the near poor became even poorer as their spending could no longer keep up with the rising poverty line. It is hoped that during this reform period, the Mojokerto Regency Government will be able to overcome the impacts and consequences and find solutions or steps to reduce the current poverty problems in Mojokerto Regency.

Robust Spatial-Temporal Analysis of Toddler Pneumonia Cases and its Influencing Factors

Pneumonia is a disease that causes inflammation of the lungs and is one of the most common diseases infecting toddlers. As a directly infectious disease, there is a possibility of the influence of location diversity on the number of pneumonia sufferers. Robust Geographically and Temporally Weighted Regression (RGTWR) is a method used to model data by considering the heterogeneity of location and time and to overcome outliers in the data. The data used is the number of pneumonia sufferers aged under five and the factors that are thought to influence it, namely the number of health centers, population density, percentage of children under five with complete basic immunizations, percentage of children under five who are exclusively breastfed 0-6 months, and percentage of poor people. This study was conducted to model pneumonia sufferers under five and to find out the factors that significantly affect the number of sufferers in each observation. RGTWR produces an optimal model with an R2 value of 99.9997%, a Mean Absolute Deviation of 21.6852, and a Median Absolute Deviation of 6.9661 compared to the Geographically and Temporally Weighted Regression model. Variables number of puskesmas, percentage of infants with complete basic immunization, and percentage of poor population are factors that influence the number of pneumonia sufferers under five in most locations in 34 provinces and 5 years of observation.

The Effect of ICT Index, Constant GRDP, HDI, and Gini Ratio by Province on Percentage of Poor Population (P0) in Indonesia, 2019-2021

Digital and information technology has evolved very rapidly in the wake of the Covid-19 pandemic. This affects all spheres of life of the Indonesian people. The Information and Communication Technology Development Index (IP-TIK) is a standard measure to describe a region's information and communication technology development level, digital divide and ICT development potential. A region's population poverty rate, regional GDP growth, Human Development Index and Gini coefficient all affect the region's Information and Communication Technology Index (ICT Index). In this study, the authors wanted to know which variables had the greatest impact and how these variables affected scores on the Information and Communication Technology Index (ICT). Research methods use literature research data and quantitative methods. This study uses convergent validity, discriminant validity and composite reliability testing tools. The researchers used SmartPLS 3.2.9 software to determine R-squared values using partial least squares (PLS). The R-squared test results show that the R-squared value of the variable IP ICT is 0.906, which is interpreted as the ability of the model "Percentage of poor population in each province and region (P0), constant GRDP, HDI". It is OK. The Gini coefficient of each province explaining the ICT Index variable is 90.6%, and the remaining 9.4% is explained by other variables.

Simulation Of The K-Means Clustering Algorithm With The Elbow Method in Making Clusters Of Provincial Poverty Levels in Indonesia

One way to ensure that government programs and assistance for each province are right on target is to create a model of grouping or clustering provinces in Indonesia based on poverty levels. Algorithm K Means is one of the clustering methods in Data Mining to divide n observations into k groups so that each observation is in the group with the closest mean. In this study, provincial poverty level clustering in Indonesia will be made based on three poverty level indicators, namely the Percentage of Poor Population (P0), Poverty Depth (P1), and Poverty Severity (P2) with the K-Means Algorithm using the Elbow Method assisted by the Python Program. The results obtained are 5 optimal clusters of provincial poverty rates in Indonesia.

Analysis of the Effect of the Gini Ratio, Percentage of Poor Population, GRDP, HDI, and Average Per Capita Expenditures on Development Inclusivity Index in Java Island

This study aimed to analyze whether the Gini ratio, the percentage of poor people, Gross Regional Domestic Product (GDP), Human Development Index (IPM), and average per capita expenditure affect the inclusiveness index of development in Java Island. The method used is panel data regression, namely choosing the best model between the fixed effect model, random effect model, and common effect model and using the classic assumption test. This research is quantitative. The data used in this study are data obtained from BPS and Bappenas from 2014 to 2020. The results show that the factor that has a significant effect on the development inclusiveness index is the Gini ratio with a significance value of 0.0225, the percentage of poor people with a significance value of 0.0015, and Gross Regional Domestic Product (GRDP) with a significance value of 0.0174. Meanwhile, the Human Development Index (IPM) and per capita expenditure have no significant effect on the development inclusiveness index in Java.

Analisis Faktor-Faktor yang Memengaruhi Angka Partisipasi Kasar SMA/Sederajat di Indonesia Menggunakan Regresi Ridge

One indicator of education in Indonesia is the gross enrollment ratio (GER). Based on data from Statistics Indonesia, the GER at the senior high school level in Indonesia is still low compared to the GER at primary and junior high schools. Summarizing the findings of prior studies, the factors affecting GER include the number of schools, percentage of poor population, education budget, and student-teacher ratio. Therefore, this study aims to examine the variables that affect GER in Indonesian senior high schools. Since multicollinearity between the predictor variables was identified, ridge regression was employed. This study found that the number of senior high schools, the percentage of the poor population, and the education budget simultaneously had a significant effect and contributed 71.39% to the GER at the senior high school level in Indonesia. It is remarkable to observe that, partially, the number of senior high schools, the percentage of poor people, and the education budget had no direct effect on the GER. Additionally, there was a positive correlation between the variables and GER. The number of senior high schools and the education budget have a favorable impact. In contrast, the percentage of poor people has a negative effect on GER. Meanwhile, the student-teacher ratio does not have a linear relationship with the GER at the senior high school level in Indonesia.

Determinants of Multidrug-Resistant Pulmonary Tuberculosis in Indonesia: A Spatial Analysis Perspective

Tuberculosis is caused by Mycobacterium Tuberculosis (MT). MT usually attacks the lungs and causes pulmonary-tuberculosis. Tuberculosis cases in Indonesia keep increasing over the years. The presence of Multidrug-Resistant Tuberculosis (MDR-TB) has been one of the main obstacles in eradicating tuberculosis because it couldn’t be cured using standard drugs. In fact, the success rate of MDR-TB treatment in 2019 at the global level was only 57 percent. Research on MDR-TB can be related to the spatial aspect because this disease can be transmitted quickly. This study aims to obtain an overview and model the number of Indonesia’s pulmonary MDR-TB cases in 2019 using the Geographically Weighted Negative Binomial Regression (GWNBR) method. The independent variables used in the model are population density, percentage of poor population, health center ratio per 100 thousand population, the ratio of health workers per 10 thousand population, percentage of smokers, percentage of the region with PHBS policies, and percentage of BCG immunization coverage. The finding reveals that the model forms 12 regional groups based on significant variables where GWNBR gives better results compared to NBR. The significant spatial correlation implies that the collaboration among regional governments plays an important role in reducing the number of pulmonary MDR-TB.

Synchronization of Poverty Reduction Program by TKPKD in Murung Raya District

In line with this, the phenomenon that occurred in Central Kalimantan Province with macro achievement indicators below the national rate, namely the poverty rate of 5.17%, the Gini index of 0.342 and the Open Unemployment Rate of 4% below the national rate of 5.13%. Open unemployment in Central Kalimantan is still dominated by residents aged 15-19 years (high school age) at 31.52%. Meanwhile, Central Kalimantan's economic growth of 6.74% is still above the national economic growth of 5.27% where the welfare of the poor tends to be stagnant when compared to the upper middle class. Murung Raya Regency is one of the Regencies in Central Kalimantan Province, the Regency Capital is located in Puruk Cahu, This Regency is a division of North Barito Regency in 2002 with an area of 23,799 km2 and a population of 111,500 people. In this case it can also be described the data on the Poverty Line, Number, and Percentage of Poor Population in Murung Raya Regency, 2017-2021.

Analysis of School Participation Rates and Their Implications on Poverty Level in Mediation of Per Capita Income in Hulu Sungai Utara Regency

Poverty is the inability to meet the minimum standard of basic needs caused by various factors such as low access to education. This study aims to analyze the effect of school enrollment rates on per capita income, the effect of school enrollment rates and per capita income on the percentage of poor people, and the effect of school enrollment rates on the percentage of poor people through per capita income. This study uses a quantitative descriptive approach. Sampling in this study uses purposive sampling. The unit of analysis is data on School Participation Rate (APS), Percentage of Poor Population, and income per capita (GDP per capita) in Hulu Sungai Utara Regency. Furthermore, the data were analyzed using descriptive analysis and inferential analysis. The results showed that there was no significant effect of the school enrollment rate on per capita income, there was a significant effect of the school enrollment rate on the percentage of the poor population, and there was no negative and statistically significant effect of the per capita income on the percentage of the poor population, there was a negative mediating effect of the variable number School participation through per capita income to the percentage of poor people.

Karakteristik Kabupaten dan Kota di Indonesia dengan Masalah Gizi Ganda pada Penduduk Dewasa Tahun 2018

Economic transitions, industrialization, urbanization, and technological shifts in developing countries have led to nutrition transitions, which has impact on the incidence of double burden malnutrition. This study aims to identify districts/cities with adult double burden malnutrition (DBM) as well as to analyze the characteristics of the region. The design of this research is an ecological study. Samples are 371 districts/cities with adult nutritional problems based on Riskesdas 2018, which categorized into undernutrition, overnutrition, and DBM region. Regional characteristics are classified based on the national figures. Samples were analyzed by spatial and bivariate analysis. There were 38 districts/cities (10.2%) in Indonesia that experienced adult DBM, with the highest number on Java island. The PPH score (87.01), protein intake (63.92 grams/cap/day), proportion of population with sufficient physical activity (62.93%), percentage of poor population (7.95 %), population density (2968 people/km2) and average length of schooling (9.35%) were not significantly different between DBM and overnutrition region (p>0.05). Harvested area (26 890.58 Ha), rice production (152.899.76 Tons/GKG), and rice productivity (152.899.76 Ku/Ha) were not significantly different between DBM and undernutrition region (p>0.05). The DBM region is a districts/cities that has undergone agricultural and economic transformation, resulting in changes in food consumption patterns and physical activity of the population. The incidence of adult DBM in districts/cities is more related to regional characteristics that increase the prevalence of overnutrition.
 Keywords: adult double burden malnutrition, nutrition transition, ecological study

The Effect of Human Development Index and Net Participation Rate on the Percentage of Poor Population: A Case Study in Riau Province, Indonesia

This study aims to determine the factors that can affect the percentage of poverty in the province of Riau Indonesia. This study using variables that are considered to affect the Poverty Percentage, namely; Human Development Index and Net Participation Rate. By using Multiple Linear Regression Analysis, the results of the study concluded that the Human Development Index and the Pure Participation Rate of Riau Province on the Poverty Percentage were 83.6 percent while the rest were still influenced by other variables. This study uses secondary data from the Central Bureau of Statistics of Riau Province by looking at 12 districts in Riau Province. This study uses quantitative research where the data analysis technique uses SPSS software. Hopefully, in the future, there will be research that looks at other factors that cause poverty levels in the Riau province of Indonesia.

Transmission of Special Autonomic Funds in the Economy through Mediation Variables

This study analyzes the effect of special autonomy funds on physical infrastructure, education, health, and poverty as well as its effect on economic growth in Aceh Province. The effect seen is the direct or indirect effect that occurs between the realization of special autonomy funds on economic growth in Aceh Province. The physical infrastructure variables represented by the length of the road, education represented by the average length of schooling, health represented by life expectancy, and poverty represented by the percentage of poor population were intervening variables. The intervening variable is a variable that is considered capable of mediating between the independent variables and the dependent variable. The analysis model used in this study is path analysis so as to be able to see the direct and indirect effects of an independent variable on the dependent variable. The results found in this study are the realization of special autonomy funds has a direct effect on economic growth. While the indirect effect is given by the variable realization of special autonomy funds on economic growth through the length of the road, life expectancy, and the percentage of poor people. The variable of average length of schooling does not have an indirect effect between the realization of special autonomy funds on the economic growth of Aceh Province.

Analisis Regresi Probit Biner Bivariat

Bivariate binary probit regression is a regression analysis that uses two dependent variables and each has two categories. This regression analysis is used on education index data and expenditure index of district/city on Kalimantan island in 2017. The best model obtained in this regression analysis is a model that uses 4 independent variables namely APS 16-18 years, percentage of poor population, open unemployment rate, and GRDP ACMP (Gross Regional Domestic Product at Current Market Prices). The parameters that significantly influence the two dependent variables are the APS 16-18 years in models 1 and 2 and the percentage of poor people in model 2. In Samarinda, every change of the APS 16-18 years, the percentage of poor people, and the open unemployment rate of 1 the unit will increase the probability of Samarinda entering the education index and high expenditure index categories by 0,33 percent, 0,42 percent and 0,07 percent, respectively. Every change of GRDP ACMP by 1 unit will reduce the probability of Samarinda entering the education index and the high expenditure index by 1,63 percent.

Analisis kluster Kabupaten dan Kota di Provinsi Jambi berdasarkan indikator pembangunan

The purpose of this study is to classify Regencies and Cities in Jambi Province based on HDI, economic growth, percentage of population growth, percentage of poor population, percentage of open unemployment rate and Gini ratio. To answer these objectives the hierarchical method used is Average Linkage (Between Groups). The of analysis were obtained, two clusters could be formed, where group 1 consisted of 10 Regencies and Cities, group 2 consisted of 1 Regencies.

Modeling of West Java inflation with Spatial Durbin Model (SDM)

Inflation in Indonesia, in general, can be caused by a combination of various factors, both in terms of demand, supply, and expectations. The contribution of each element that influences inflation is not always the same from time to time. Gordon (1997) explains the determinants of inflation from these three sides, namely demand, supply, and expectations. The importance of expectations in influencing increase was also stated earlier by Chopra (1985). In this study, spatial Durbin for panel data with fixed effects in modeling inflation in the West Java province. The results show that Minimum Work Wage, percentage of poor population, Gross Regional Domestic Product (GRDP), spatial lag of poor population, and spatial lag of GRDP have a significant influence on the Durbins panel spatial model. There is an adjacent regional effect on inflation in the West Java province based on the value of the lambda obtained. Based on the criteria R-square of 94.29%, it can be concluded that the SDM panel data model has an excellent performance in explaining the diversity of data. The AIC and BIC criteria also show that the SDM panel data model well applied in modeling Regency/City inflation in West Java province.