Vector Autoregressive (Var) Untuk Peramalan Harga Saham PT Indofood Sukses Makmur Tbk.

In simultaneous or structural equation models, if there is true simultaneity among a set of variables, they should not be any apriori distinction between endogenous and exogenous variables. It is in this spirit of the VAR models. We study stock value simultaneous model at PT Indofood Sukses Makmur Indonesia Tbk, for the period of 1998–2005. Variables in stock value simultaneous models are stock value, Return of Assets (ROA), Debt to Equity Ratio (DER), and Earning Per Share (EPS). The data is obtained from the Indonesian Capital Market Directory, Jakarta Stock Exchange and PT Indofood Sukses Makmur Indonesia Tbk. Financial Statement. The empirical results showed that the variables under consideration can be said to be integrated of order one, and the residuals have independent and normal distribution. With AIC and SC values, we conclude that each equations contains four lag values of Stock Value, ROA, DER, and EPS. The estimated VAR model can be used for forecasting the future values of stock value. In this study, we forecast the stock value from 2006:1 until 2007:12

Analisis Korelasi Kanonik pada Perilaku Kesehatan dan Karakteristik Sosial Ekonomi di Kota Pati Jawa Tengah

One of the general problem that is social and economics which not yet flatten and still meeting of low health case. To know the correlation between social and economics characteristic and behavior of health in Pati of Central Java is used the canonical correlation analysis. The variable that is taken are social and economics characteristic and behavior of health variable, which each consisting of nine indicator variable. Some assumptions like linearity, normal multivariable and do not multicolinearity should be fulfilled. After the assumption have fulfilled, data processing can be done so that obtained a conclusion. The result of canonical correlation analysis indicate that there is a signifikan correlation between social and economics characteristic variable and behavior of health variable. From nine indicator which forming variable of social and economics characteristic, earnings indicator variable, education of mother, expenditure and education of father giving the most dominant of contribution. While from nine behavior of health variable, indicator of balanced nutrient variable, physical activity, eradication of mosquito den, house floor, exclusive ASI, and brush teeth giving the most dominant of contribution. Keywords : Social and Economics Characteristic, Behavioral of Health, Canonical Correlation Analysi

Analisis Faktor-faktor yang Mempengaruhi Kesembuhan Pasien Penyakit Flu Burung

Avian influenza is contagion which caused by influenza virus type H5N1 often cause death. Avian influenza anticipated to be influenced by gender, age, epidemiology and case, to know the factors have a significant effect used by independent test. is later on made model of regresi binary logistics. Then obtained by factor having an effect is case and epidemiology, that is made regression logistics model. Someone which including case of suspect to be able to have probability recover bigger than someone which including confirmation case, someone which contact with dead an avian to be able to have probability recover smaller than someone which no contact

Komponen Utama untuk Pengendalian Kualitas secara Statistik

Statistically Quality Control is a problem solving technique that used to check, control, analyze, bring off and repair product with statistical methods. One of the method that used statistically control quality is Principal Component Analysis. Principal Component Analysis is a multivariate technique that used to reduce the dimension of data. Principal component is concerned with explaining the variance-covariance structure of a set of variables through a few linear combinations of these variables. Statistically quality control with principal components is used by constructing multivariate control charts which consist of Ellipse Chart for first two principal components and Chart for unexplained principal components in Ellipse Chart

Analisis Regresi Linier Piecewise Dua Segmen

Regression analysis is a statistical method that is widely used in research. In general, the regression analysis is the study of the relationship of one or more independent variables with the dependent variable. In analyze the functional relationship between X as the independent variables and Y as the dependent variable, there may be a linear relationship is different for each interval X. If the regression of X on Y has a linear relationship on the certain of the interval of X, but also has a distinct linear relationship at another interval of X, so the use of piecewise linear regression is appropriate in this case. Piecewise linear regression is a method in regression analysis that divided the independent variable into several segments based on a particular value called the X-knots, and in each segment of the data contained linear regression model. X-knot is a value on the independent variable, where X is the current value of the X-knots, it will form a linear regression equation of the line that is different than the current value of X is under X-knots. Piecewise linear regression can be applied in many fields, one of them in the waters of the analysis regarding the influence of river discharge on the basis of the number of transport sediman. By comparison MSE simple linear regression and multiple linear piecewise two segments, the result that the two segments piecewise linear regression is a model that describes the influence of river discharge on the basis of the number of bedload transpor

Regresi Robust Mm-estimator Untuk Penanganan Pencilan Pada Regresi Linier Berganda

The multiple linear regression model is used to study the relationship between a dependent variable and more than one independent variables. Estimation method which is the most frequently be used to analyze regression is Ordinary Least Squares (OLS). OLS for linear regression models is known to be very sensitive to outliers. Robust regression is an important method for analyzing data contaminated by outliers. This paper will discuss the robust regression MM-estimator. This estimation is a combined estimation method which has a high breakdown value (LTS-estimator or S-estimator) and M-estimator. Generally, there are three steps for MM-estimator: estimation of regression parameters initial using LTS-estimators, residual and robust scale using M-estimator, and the final estimation parameter using M-estimator. The purpose of writing this paper are to detect outliers using DFFITS and determine the multiple linear regression equations containing outliers using robust regression MM-estimator. The data used is the generated data from software Minitab 14.0. Based on the analysis results can be concluded that data 21st, 27th, 34th are outliers and equation of multiple linear regression using robust regression MM-estimators is

Multivariate Adaptive Regression Splines (Mars) Untuk Klasifikasi Status Kerja Di Kabupaten Demak

Unemployment is one of the issues relating to economic activities, public relations and also the problems of humanity. Unemployment also occur in Demak and factors suspected as the cause of unemployment in Demak: gender, area of residence, age, status in the household, marriage status and education. Demak BPS records the number of people looking for work (unemployed) as many as 226.228 people, or 29,55% of the working age population. MARS (Multivariate Adaptive Regression Splines) is one of the methods used for classification. MARS is used for high-dimensional data, which is data that has a number of predictor variables for 3 ≤ v ≤ 20 data used in this study is a secondary data from national labor force survey (SAKERNAS) in 2012. To get the best MARS models performed with by combining Maximum Base Function (BF), Minimal Observation (MO), and Maximum Interaction (MI) by trial and error. MARS model is used to classify employment status in Demak are MARS models (BF =24, MI=3, MO=1)

Klasifikasi Kelompok Rumah Tangga Di Kabupaten Blora Menggunakan Multivariate Adaptive Regression Spline (Mars) Dan Fuzzy K-nearest Neighbor (Fk-nn)

Good classification method will result on less classification error. Classification method developed rapidly. Two of the existing classification methods are Multivariate Adaptive Regression Spline (MARS) and Fuzzy K-Nearest Neighbor (FK-NN). This research aims to compare the classification of poor household and prosperous household based on per capita income which has been converted according to the poverty line between MARS and FK-NN method. This research used secondary data in the form of result of National Economy and Social Survey (SUSENAS) in Blora subdistrict in 2014. The result of the classification was evaluated using APER. The best classification result using MARS method is by using the combination of BF= 76, MI= 3, MO= 1 because it will result on the smallest Generalized Cross Validation (GCV) and the APER is 10,119 %. The best classification result using FK-NN method is by using K=9 because it will result on the smallest error and the APER is 9,523 %. The APER calculation shows that the classification of household in Blora subdistrict using FK-NN method is better than using MARS method

Pemodelan Indeks Harga Saham Gabungan (Ihsg) Menggunakan Multivariate Adaptive Regression Splines (Mars)

Composite Stock Price Index (CSPI) is a historical information about the movement of joint-stock until a certain date. CSPI is often used by inventors to see a representation of the overall stock price, it can analyze the possibility of increase or decrease in stock price. Following old examination, some economy macro variables affecting CSPI is inflation, interest rate,and exchange rate the Rupiah againts the u.s.dollar. MARS method is particularly suitable to analyze a CSPI because many variables that affected. Furthermore, in the real world is very difficult to find a spesific data pattern. The analysis is MARS analysis. The purpose is an obtained a MARS model to be used to analyze the CSPI movement\u27s. Selection MARS model can be used CV method. The MARS model is an obtained from combination of BF, MI, dan MO. In this case, happens the best models with BF=9, MI=2, dan MO=1. Accuracy for MARS model can see MAPE values is 14,32588% it means the model can be used

Ketepatan Klasifikasi Keikutsertaan Keluarga Berencana (Kb) Menggunakan Analisis Regresi Logistik Biner Dan Fuzzy K-nearest Neighbor in Every Class Di Kabupaten Klaten

Fertility is one of the factors that affect population growth. High population growth resulted in the emergence of a variety of problems for a country including Indonesia. This requires a treatment that population growth can be controlled, one attempts to handle by using a Keluarga Berencana program. Therefore conducted a study to determine the factors that affect that participation of Keluarga Berencana (KB) by using Binary Logistic Regression analysis in which the participation of KB divided into two, namely join KB and KB did not participate. Based on the results obtained Binary logistic regression analysis predictor variables that significantly affect participation KB is the number of children, father's education, and mother's education. The resulting classification accuracy with training data comparison testing was 90:10 at 84.375%. Furthermore, the data were analyzed by using Fuzzy K-Nearest Neighbor in every Class (FK-NNC) to determine the accuracy of the classification results comparison with FK-NNC Binary Logistic Regression. From the analysis of the classification accuracy using the FK-NNC with a 90:10 ratio of training data and testing the value of K = 7 values obtained tersebesar ie 87.5%. The comparison of classification accuracy of this value indicates if the FK-NNC is better classify participation in Keluarga Berencana in Klaten district 2012