There are many types of investments that can be used to generate income, such as in the form of land, houses, gold, precious metals etc., there are also in the form of financial assets such as stocks, mutual funds, bonds and money markets or capital markets. One of the investments that attract enough attention today is the capital market investment. The purpose of this study is to predict and improve the accuracy of foreign exchange rates on forex business by using the Support Vector Machine model as a model for predicting and using more data sets compared with previous research that is as many as 1558 dataset. This study uses currency exchange rate data obtained from PT.

The problems in employment was the growing number of Open Unemployment Rate (OUR). The open unemployment rate is a number that indicates the number of unemployed to the 100 residents are included in the labor force. The purpose of this study is mapping the data OUR in Central Java and the suspect and identify linkages between factors that cause OUR in the District / City of Central Java in 2014. Factors that allegedly include population density (X 1 ), Inflation (X 2 ), the GDP value (X 3 ), UMR Value (X 4 ), the percentage of GDP growth rate (X 5 ), Hope of the old school (X 6 ), the percentage of the labor force by age (X 7 ) and the percentage of employment (X 8 ). Geographically Weighted Regression (GWR) is a method for modeling the response of the predictor variables, by including elements of the area (spatial) into the point-based model. This research resulted in the conclusion that the OLS regression models have poor performance because the residual variance is not homogeneous. There were no significant differences between GWR models with OLS model or in other words generally predictor variables did not affect the response variable (rate of unemployment in Central Java) spatially. However, GWR model could captured modelling in each region.

The implementation of technology in learning becomes imperative and needs. Through learning, technology will be more meaningful and facilitate students in comprehending the materials. Based on the evaluation of the multivariable calculus courses can be concluded that the students had much difficulty in drawing materials graphic in dimensional space 3 (R3). Students had not been able to draw graphs of linear and quadratic equations well. To solve the problem, the researchers implemented the Maple software on the multivariable calculus courses. This study used a qualitative approach with a descriptive analysis method. The subject of research is the students’ of Mathematics education of Muhammadiyah Semarang University in Multivariable calculus courses in the academic year 2019/2020. The stages of learning have six stages, such as; 1) Students are introduced Cartesian coordinates in R3; 2) Students are given student worksheets; 3) Students are required to draw graphics in the Maple software; 4) Students are asked to compare pictures in student worksheets and Maple; 5) Students present discussion results; 6) Lecturers give feedback and conclude learning. Based on the research results, it can be concluded that the implementation of Maple Software can improve the ability of students’ spaces in Multivariable calculus courses.

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Spline regression is a nonparametric regression method that estimates data patterns that do not form certain patterns with the help of knots. The best model is obtained from the optimal knot. There are several methods that can be used to select optimal knots, including Generalized Cross-Validation (GCV) and Unbiassed Risk (UBR). The best model selection criteria used are based on the Mean Squared Error (MSE) and R-Square values. This study discusses the comparison of spline regression models using the UBR and GCV methods as a method for selecting optimal knots in data generation simulations. This research resulted in the best nonparametric spline regression model from the UBR method obtained by using three knots which produced an MSE value of 738.67 and R -Square of 85.65%. Whereas, the best nonparametric spline regression model of the GCV method was obtained using three knots which produced an MSE value of 121.43 and R-Square of 97.64%. It can be concluded that the more appropriate method used for the selection of optimal knot is the GCV method because it produces a smaller MSE value and a larger R-Square compared to the UBR method.

Indonesia is an agricultural country with rice as one of the staple foods. Production of rice in the province of Central Java is the highest in Indonesia. The purpose of this study was to model rice production in 31 districts / cities in Central Java Province using semiparametric regression. Semiparametric regression is a combination of parametric and nonparametric regression. Parametric regression curves have a patterned, for example linear, quadratic, and cubic. Nonparametric regression has a smooth curve of the unknown pattern, so in this case required smoothing technique used to smooth curves that one of them is the local polynomial kernel approach and the election of bandwidth the optimal using method Generalized Cross Validation (GCV). Variables used in the study of the production of rice as the response variable, while the predictor variables that harvested area and rainfall. The data used are secondary data from the official website of Central Bureau of Statistics (BPS) of Central Java. Based on the results obtained by applying the model the optimal bandwidth values is 0.43 and polynomial order p = 2 when the minimum GCV so the results of the estimation model R2 is 0.968

Rice is one of staple food in Central Java province because rice is the main carbohydrate and calorie source for society in general. From year to year rice production in various regions in Indonesia shows a significant increase. Central Java is one of the provinces in Indonesia which has the agricultural sector as its main sector. However, in the last five years, the average rice production in Central Java showed a stagnant decline in value. This study was aimed to model the spatial effects on rice productivity in the cities in Central Java along with the factors that influence it. The method used is spatial modeling approach. The results of the analysis show that spatial lag X (SLX) model has the smallest AIC value, estimation result shows that rice production and harvest area have significant effect on rice productivity in Central Java.

HIV / AIDS is a contagious disease that can attack all age groups of the population and is a health challenge in almost all over the world including Indonesia. Therefore, it is necessary to model HIV / AIDS cases for the factors that are suspected to influence them. One suitable method for estimating factors that influence HIV / AIDS is the Generalized Additive Model for Location, Scale, and Shape (GAMLSS). The GAMLSS method is flexible because it includes expansion of a good exponential family distribution to handle overdispersion data, continuous data, and discrete data. This research will apply GAMLSS semiparametric modeling with LOESS smoothing to find out the characteristics and models of HIV / AIDS cases in East Java in 2017. Based on the analysis, it was found that the variables that significantly affected were the number of homeless people, number of victims of drug abuse, population poor, and the number of fertile age couples using condom contraception with AIC value of 437,404, degree = 1 and span = 0.3, and the distribution used is Negative Binomial I.