Percentage Of Population Density Research Articles

Environmental Quality Analysis of Settlement Around the Electric Steam Power Plant (PLTU) Palabuhanratu, Sukabumi, West Java

According to the Central Statistics Agency, Indonesia's population growth rate will reach 1.17% in 2022 and will continue to increase exponentially annually. Moreover, the increasing number of population growth leads to several issues, including environmental quality of settlement. This study examined the quality of the environmental settlement around the Electric Steam Power Plant (PLTU) Palabuhanratu. The data used in this study is a combination of open-source data and field survey data. The method used is an interpretation technique (satellite imagery) assisted by geographic information system (GIS) technology with six-parameters, there are settlement density, settlement layout, settlement location, road conditions, building conditions and availability of health facilities. The results of this study are information including settlement quality and recommendations related to the quality of the environmental settlement around the PLTU Palabuhanratu which can be used as input in spatial planning in the Palabuhanratu area for sustainable regional development. Overall, this research shows that the environmental quality of settlements around PLTU Palabuhanratu is dominated by good category. However, it is possible that the quality of settlements will deteriorate in the future because the percentage of population density which almost close to 40%.

APPLICATION OF NONPARAMETRIC GEOGRAPHICALLY WEIGHTED REGRESSION METHOD ON OPEN UNEMPLOYMENT RATE DATA IN INDONESIA

Nonparametric Geographically Weighted Regression (NGWR) model is a development of nonparametric regression with geographic weights for spatial data where parameter estimators are local to each observation location. NGWR is used to obtain the best model for the Open Unemployment Rate (OUR) data in Indonesia. Unemployment is still a significant social and economic problem in Indonesia. This study aims to obtain the NGWR model on the OUR data in Indonesia and to determine the factors that significantly affect OUR. The method used is the NGWR model with bisquare kernel function weighting and gaussian kernel function. The best model is obtained by NGWR with bisquare kernel function weighting at order 1 and knot point 1, with R2 is 83.45 percent which explains that the predictor variables affect the OUR by that number. The factors that have a significant effect on OUR are the percentage of population density, minimum wage, average years of schooling, GRDP, and the percentage of poor people.

Poisson Regression Modeling of Diarrhea Events in Pasuruan Regency with Maximum Likelihood Estimates and Generalized Method Moment.

Context:Diarrhea characterized by a frequency increased of defecation more than 3 times/ day accompanied by changes in consistency (becoming liquid). The causes of diarrhea can be divided into 2 parts, which are direct causes and indirect causes that can facilitate or accelerate the occurrence of diarrhea, including bacteria, nutritional conditions, hygiene and sanitation, social culture such as population density, economic status, low birth weight, and immunization.Aims:The purpose of this study to examine the factors that influence the incidence of diarrhea.Methods:This research used secondary data, the prevalence of diarrhea and risk factors in Pasuruan Regency Health Center. Poisson regression approach with maximum likelihood estimator (MLE) estimation and Generalized Method Moment (GMM) used in this study.Results:The results showed that GMM estimation method in the Poisson regression model gave better performance in terms of significance parameters compared to the MLE method.Conclusions:Factors affecting the increase of diarrhea occurrences in area with an estimated MLE Percentage of non-exclusive breastfeeding and Percentage of normal nutritional status. Whereas the GMM estimation is the percentage of non-exclusive breastfeeding, the percentage of low birth weight, the percentage of population density, the percentage of smokers among family members in the house, the percentage of incomplete immunizations, the percentage of under-five years old children less than 2, the percentage of normal nutritional status, and the percentage of middle class socioeconomic status.

Yoga training reduces maternal stress during pregnancy

As an effort of prevention of Dengue Fever Occurence, it must be applied an information system which is able to give fast, exact, complete, and easy understood information. Because of that, it needs to be developed a Geographical Information System {Sistem Informasi Geografis (SIG)} in society health service, that is related to the distribution and risking factors of Dengue Fever Occurence. The Geographical Information System model can show the description of spacial distribution of Dengue Fever cases in a district, the tendency of illness spreading, and whether there is clustering of the illness or not, so it can identify unsafe/susceptible level in a district to Dengue Fever Occurrence.The design of this research is observational analytic with Cross Sectional approach, using Geographical Information System in spacial analysis. The samples of the research are all dengue fever victims recorded at public health centre in Ngemplak Subdistrict, Boyolali Regency from 2016 to 2017, which are 93 respondents. The data analysis is done by univariate, bivariate with Contingency Coefficient test and spacial analysis in Geographical Information System approach.The result of the recearch shows risking factors that is proved significantly related to dengue fever occurence. Those are population density percentage (p = 0.003) and the number of free mosquito larva percentage (p = 0.001). Spacialy, districts in Ngemplak Subdistrict, Boyolali, that have highest susceptible level to dengue fever are Sindon and Gagak Sipat village.

Special model of susceptible district of dengue fever in Ngemplak Subdistrict, Boyolali

As an effort of prevention of Dengue Fever Occurence, it must be applied an information system which is able to give fast, exact, complete, and easy understood information. Because of that, it needs to be developed a Geographical Information System {Sistem Informasi Geografis (SIG)} in society health service, that is related to the distribution and risking factors of Dengue Fever Occurence. The Geographical Information System model can show the description of spacial distribution of Dengue Fever cases in a district, the tendency of illness spreading, and whether there is clustering of the illness or not, so it can identify unsafe/susceptible level in a district to Dengue Fever Occurrence. The design of this research is observational analytic with Cross Sectional approach, using Geographical Information System in spacial analysis. The samples of the research are all dengue fever victims recorded at public health centre in Ngemplak Subdistrict, Boyolali Regency from 2016 to 2017, which are 93 respondents. The data analysis is done by univariate, bivariate with Contingency Coefficient test and spacial analysis in Geographical Information System approach. The result of the recearch shows risking factors that is proved significantly related to dengue fever occurence. Those are population density percentage (p=0.003) and the number of free mosquito larva percentage (p=0.001). Spacialy, districts in Ngemplak Subdistrict, Boyolali, that have highest susceptible level to dengue fever are Sindon and Gagak Sipat village.