This study aims to estimate the regression parameters fixed effects panel data model using the first difference method on the influence of Life Expectancy, Average Length of School, and Per capita Expenditure on the Human Development Index of South Sulawesi in 2012 - 2018. The first difference method is used to obtain intercept differences in each district/city explaining the effect of regional differences. The first difference process results in autocorrelation of data so after the first difference is done the generalized least square method is used to estimate the parameters. The results show Life Expectancy, Average Length of School, and Per capita Expenditure has a significant influence on the Human Development Index of South Sulawesi in 2012 - 2018 simultaneously or partially.

Nonparametric regression is a regression approach that is used when one of the parametric assumptions are not fulfilled. One of the estimators in nonparametric regression is penalized spline. The growth pattern of toddler that varied each month of observation make the suitable regression approach is nonparametric penalized spline regression because of its high flexibility. This study aims to obtain an estimate of the growth model for toddler in South Sulawesi. The optimal model obtained with a minimum GCV value of 4.87E-05 using two point knots that is 14 and 56 with lamda 100. The estimation results show that there are 3 intervals of change patterns in the growth of toddler in South Sulawesi

Binary logistic regression is a method to describe the relationship between response variable that has two categories and one or more predictor variables. One of methods that can be used to obtain best model in logistic regression is stepwise. Stepwise method is a method that sets  and  as criteria to build model. Dominance analysis is used in this research to determine the importance rank of predictor variables by comparing the coefficient of determination  value before and after the predictor variable entered the model. Binary logistic regression can be used to find the relationship between hypertension and the factor risks. This study aims to obtain best model and to obtain the importace rank each predictor variable of binary logistic regression on data of hypertension in Indonesia. The result of this study shows that best model which is obtained is model with predictor variable of Heart Problems, High Cholesterol, Kidney Disease, Imperfect Vision, Breathlessness, and Nausea/ Vomitting. According to the value of  McFadden, predictor variable of High Cholesterol infests first rank in the importance of predictor variable or gives the greatest contributions in explaining variety of Status of Hypertension than other predictor variables.Binary logistic regression is a method to describe the relationship between response variable that has two categories and one or more predictor variables. One of methods that can be used to obtain best model in logistic regression is stepwise. Stepwise method is a method that sets  and  as criteria to build model. Dominance analysis is used in this research to determine the importance rank of predictor variables by comparing the coefficient of determination  value before and after the predictor variable entered the model. Binary logistic regression can be used to find the relationship between hypertension and the factor risks. This study aims to obtain best model and to obtain the importace rank each predictor variable of binary logistic regression on data of hypertension in Indonesia. The result of this study shows that best model which is obtained is model with predictor variable of Heart Problems, High Cholesterol, Kidney Disease, Imperfect Vision, Breathlessness, and Nausea/ Vomitting. According to the value of  McFadden, predictor variable of High Cholesterol infests first rank in the importance of predictor variable or gives the greatest contributions in explaining variety of Status of Hypertension than other predictor variables.

Statistics Indonesia explained that the percentage of households in Indonesia that had access to decent, safe and affordable housing during the 2019-2020 period was still below 60 percent. Of course, this is a big job for the government to be able to achieve the target in the RPJMN 2020-2024, which is up to 70 percent in providing decent, safe, and affordable housing for the community by 2024. This study aims to group provinces in Indonesia based on indicators. household housing by applying and choosing the best algorithm among k-means and k-medoids. Based on the selection of the best algorithm, k-means is the best algorithm in classifying provinces in Indonesia compared to k-medoids with three clusters. The results of the grouping of provinces in Indonesia are expected to assist the government in dealing with problems related to household housing indicators so that the government's target of increasing the percentage of households occupying decent, safe, and affordable housing can be achieved.

Currency exchange rate is the price of one unit in domestic currency against foreign currencies. Exchange rates change over time depending on the supply and demand for foreign exchange relative to the domestic currency. This study aims to predict changes in the USD exchange rate against the Rupiah in 2021 using the DES method. The use of the DES method in this forecasting takes into account that the data to be used has a trend which is characterized by the tendency of the data to move up and down over a long period of time. The DES method can also determine the trend equation for the second most extensive smoothing data through a smoothing process. This forecasting system captures patterns from past data and then uses it to project future data. The results of forecasting the USD exchange rate against the Rupiah from 2021 to 2022 show that the exchange rate ranges from IDR 14,512 to IDR 14,744 with a MAPE value of 1.93%.

IHPBI is an early indicator in consumer price analysis. When inflation has occurred, Indonesia's economic stability begins to be disturbed, so in order to suppress inflation, the government raises interest rates and when the circulation of money begins to decrease. This study to see IHPBI in the next 3 years through forecasting using the ARFIMA method and multi-input intervention. This is done to find out the movement of the IHPBI over the next 3 years and to compare the two methods. The results obtained show that the selected model is ARFIMA(1,0.1579,0), the January 2009 intervention with ARIMA(1,1,1) of order (b=0, s=1, r=1) and November 2013 intervention with ARIMA(1,1,2) order (b=1, s=1, r=0). The IHPBI forecast for the next 3 years is increasing slowly every month