E jurnal Matematika

6-13

Authors: IDA AYU PRASETYA UTHAMI, I KOMANG GDE SUKARSA, I PUTU EKA NILA KENCANA

In regression analysis, the method used to estimate the parameters is Ordinary Least Squares (OLS). The principle of OLS is to minimize the sum of squares error. If any of the assumptions were not met, the results of the OLS estimates are no longer best, linear, and unbiased estimator (BLUE). One of the assumptions that must be met is the assumption about homoscedasticity, a condition in which the variance of the error is constant (same). Violation of the assumptions about homoscedasticity is referred to heteroscedasticity. When there exists heteroscedas¬ticity, other regression techniques are needed, such as median quantile regression which is done by defining the median as a solution to minimize sum of absolute error. This study intended to estimate the regression parameters of the data were known to have heteroscedasticity. The secondary data were taken from the book Basic Econometrics (Gujarati, 2004) and analyzing method were performed by EViews 6. Parameter estimation of the median quantile regression were done by estimating the regression parameters at each quantile τth, then an estimator was chosen on the median quantile as regression coefficients estimator. The result showed heteroscedasticity problem has been solved with median quantile regression although error still does not follow normal distribution properties with a value of R2 about 71 percent. Therefore it can be concluded that median quantile regression can overcome heteroscedasticity but the data still abnormalities. Keywords: Heteroscedasticity, Median Quantile Regression, EViews

19-23

Authors: FEMY AYU ASTITI, NI MADE ASIH, I NYOMAN WIDANA

Optimization problems can be solved by various methods, such as Lagrange Method. This method can be used to find the solution. Using Lagrange method, the extreme value can be obtained, so that the optimal solution can be found. In this research, the maximum revenue of UD. Sari Madu is a limited by several constraints. After the objective function and constraint function being model, than maximum revenue is found. From first until fourth quarterly, the maximum revenue is found Rp. 9.701.333, Rp. 10.064.148, 9.793.272 and Rp. 9.397.730 respectively. Keywords: Lagrange Method, Ekstrem Value, Optimization

33-36

Authors: KADEK FRISCA AYU DEVI, KOMANG DHARMAWAN, NI MADE ASIH

Utility function can use to give risk preference for investors who want to get the benefits gained meets investment targets. Quadratic utility functions on optimal portfolio is strongly influenced by the expected return and standard deviation. The establishment of optimal portfolios using a quadratic utility function optimization problems. Under the settlement portfolio optimization, the necessary data is expected return, variance, and variance covariance matrix. The optimal portfolio is affected by some factors Risky less Rate, risk aversion index, and Borrow Rate. The results of settlement portfolio optimization is obtaining the utility value while the relatively large changes influencing by risk averse index. Keywords: Portofolio optimal, Historical data, utility function

42-48

Authors: KADEK RISNA WITARI, I.G.K. GANDHIADI, I PUTU EKA NILA KENCANA

The closeness of the relationship between dogs and human makes the dog can be trained, played together, lived together with human and are invited to socialize with human and other dogs. But, despite its this relationship, the dogs can also have a contagious disease that may be harmful to human. Diagnostic expert system for infectious disease in dogs for as web-based system with the media android used as a tool for diagnosing infectious diseases in dogs based on physical characteristics or symptoms that can be seen or felt without through examination of the specifics. The diagnostics final results are expected to be a reference for further examination and handling of dogs that contract the disease is detected to prevent the disease for humans. This expert system uses a forward chaining inference method and Certainty Factor to infer. Keywords: expert systems, forward chaining, certainty factor

54-59

Authors: NI KETUT TRI UTAMI, I KOMANG GDE SUKARSA,I PUTU EKA NILA KENCANA

Ordinary least square is parameter estimation method for linier regression analysis by minimizing residual sum of square. In the presence of multicollinearity, estimators which are unbiased and have a minimum variance can not be generated. Multicollinearity refers to a situation where regressor variables are highly correlated. Generalized Ridge Regression is an alternative method to deal with multicollinearity problem. In Generalized Ridge Regression, different biasing parameters for each regressor variables were added to the least square equation after transform the data to the space of orthogonal regressors. The analysis showed that Generalized Ridge Regression was satisfactory to overcome multicollinearity. Keywords: Linear regression, parameter estimation, multicollinearity, Generalized Ridge Regression