The high population growth rate can impact various fields due to several factors. Some of the impacts of this high rate are high poverty rates, unemployment, consumption levels, inequality in education figures, gender empowerment index, and increasingly narrow land or area. Therefore, research on the rate of population growth using data on poverty, unemployment, consumption levels, education rates, gender empowerment index, and area makes sense. This data was taken from the official website of the Central Statistics Agency for six provinces on the island of Java, Indonesia. The data used contains missing data so that the missing data is presumed by using the k-nearest neighbour method. The estimated missing data values were modelled using binary logistic regression. Variables that significantly affect the rate of population growth, namely the level of consumption, gender empowerment index, and area, are obtained using the backward stepwise method and are selected based on the smallest Aikakes criterion information value or the one with the most excellent accuracy rate.
Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the transmission can mediate human-to human by enviroment. According to Indonesian Meterological, Climatological, and Geophysical Agency found that weather and climate were supporting factors of COVID-19 outbreak so, research and analysis is carried out regarding the most factor were supporting the spread of COVID-19. In this study, using secondary data obtained from data reported by Indonesian Meterological, Climatological, and Geophysical Agency. According the aims of this study by using Principal Component Analysis (PCA) there are three principal components which represents the most factor were supporting the spread of COVID-19 they are temperature, humidity, and length of sunshine.
Educated unemployment is caused by a misalignment of educational development planning and employment development, resulting in underemployed graduates from various educational institutions. Unemployment data in DKI Jakarta shows an unequal class. Unbalanced data is a severe problem of modeling because it can cause prediction errors that affect the accuracy of the resulting model. Using SMOTE to handle unbalanced data will likely increase the model’s accuracy. This study aims to find the best model for identifying the factors influencing the status of educated unemployment using logit and probit models and handling unbalanced data using SMOTE. The results showed that the independent variables that affect the status of educated unemployment in the logit and probit models are the same: age group and participation in training. The independent variables that affect the status of educated unemployment in the logit and probit models with SMOTE are also the same: age group, marital status, and participation in training. Unbalanced data handling using SMOTE can increase the balanced accuracy value significantly. Balanced accuracy values for the logit and probit models with SMOTE are higher than the logit and probit models without SMOTE. The logit model with SMOTE is the best because it has the highest balanced accuracy value compared to other models. According to the logit model with SMOTE, the educated unemployed in DKI Jakarta are young and have never married. There is a need for the government to play a role in improving the quality of educational institutions in producing graduates who meet company qualifications and can be hired by employers. Unemployed people who have attended the training, despite having a higher education, may also become unemployed. The training provided has not been able to reduce the unemployment rate. As a result, the government should be able to provide training to improve entrepreneurship skills while also providing capital in the form of business loans to reduce educated unemployment.
COVID-19 entered Indonesia in March 2020 and included North Kalimantan Province, Tarakan. COVID-19 cases have outspread in Tarakan. The cause of the outspread and the patterns were not known yet. One relevant approach was to use Generalized Linear Models. The two methods are Poisson Regression and Stochastic with Spatial Poisson Process. The variables used were rainfall, population density, and temperature in each village in Tarakan. The Poisson Regression analysis founds that only one factor affected temperature. Then, the results were refined with the Spatial Poisson Process, where in addition to the influencing factors also, the distribution patterns are obtained. The analysis showed that the pattern of case distribution was included in the non-homogeneous Poisson process criteria. Then the model of the case density intensity was obtained using regression. From the model, it was known that the covariate variables significantly influence rainfall and temperature. Compared with general Poisson regression analysis, the results showed that only the average temperature variables had a significant effect. Thus, a better method was used, namely the Spatial Poisson Process. It was also shown by the two models' AIC values, where the AIC value of the Spatial Poisson Process model was smaller than the Poisson Regression.
Natural phenomena that can cause natural changes on earth caused by increasing greenhouse gases and decreasing landthat absorbs carbon dioxide are called climate change. The elements that cause changes include rainfall and temperature.The constantly rising temperature of the earth results in changing rainfall patterns and can have various effects on theenvironment. Therefore, research on rainfall modeling with annual average temperature and rainfall data from theprovince of East Java from 2006 to 2017 which was taken from the official website of the Central Statistics Agency ofEast Java Province makes sense. This data is a data multivariate time series that is approached with Functional DataAnalysis and modeled using Functional Prediction Regression. Functional Prediction Regression is a form of modelingwith functional data that can test the overall model for high-dimensional data and one of the improved methods ofregression methods for functional data. One way to model Functional Prediction Regression is through boosting. Thisresearch conducted rainfall modeling with Functional Prediction Regression through boosting from East Java Provinceand obtained modeling results using an additional predictor model with 5-fold bootstrap and adjustment of the splineregression (knots) used, namely 16 indicated by the iteration value-boosting bootstrap where the model used to have alinear functional effect of 𝑚𝑠𝑡𝑜𝑝 for the mu parameter and 100 for the sigma parameter.
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