E jurnal Matematika

6-10

Authors: PUTU SUSAN PRADAWATI, KOMANG GDE SUKARSA, I GUSTI AYU MADE SRINADI

Poisson regression was used to analyze the count data which Poisson distributed. Poisson regression analysis requires state equidispersion, in which the mean value of the response variable is equal to the value of the variance. However, there are deviations in which the value of the response variable variance is greater than the mean. This is called overdispersion. If overdispersion happens and Poisson Regression analysis is being used, then underestimated standard errors will be obtained. Negative Binomial Regression can handle overdispersion because it contains a dispersion parameter. From the simulation data which experienced overdispersion in the Poisson Regression model it was found that the Negative Binomial Regression was better than the Poisson Regression model.

18-26

Authors: IDA BAGUS KADE PUJA ARIMBAWA K., KETUT JAYANEGARA, I PUTU EKA NILA KENCANA

This study compares the accuracy of forecasting using ANFIS and Fuzzy Time Series the number of Australian tourists to Bali. The data used in this study are data on the number of Australia tourists visit to Bali from the period January 2006 through December 2011. ANFIS consists of two stages of learning and testing phases. Least Squares Estimator is used to study the forward direction and Error Back Propagation learning is used in the reverse direction. Forecasting with Fuzzy Time Series is forecast to capture the pattern of previous data is then used to project the data to come. The results of comparison of both methods showed that the ANFIS method has a higher forecasting accuracy than the method of Fuzzy Time Series. Forecasting by using ANFIS method obtained AFER aqual to 9,26% while the prediction using the method of Fuzzy Time Series obtained AFER aqual to 14,02% Keywords: ANFIS, Fuzzy Time Series, Forecasting, Australian Tourist Forecasting

32-36

Authors: ADE KUSUMA DEWI, I WAYAN SUMARJAYA, I GUSTI AYU MADE SRINADI

The experiments were conducted to determine the relationship between response variable and independent variable and the effect of independent variables to response variable. Multiple comparison tests can be used to find the level that makes optimal response. However, this test has a drawback that it only finds the optimal level tested at the level being considered. Thus test is more suitable for qualitative independent variables. On the other hand, for quantitative independent variables we can use the orthogonal polynomial method and response surface method (RSM).The purpose of this research is to apply RSM to produce optimal response. It can be concluded that RSM is an efficient used to find the level of independent variables to makes optimal response. Keywords: Experiments Design, Response Surface Method.

42-48

Authors: PANDE GDE DONY GUMILAR, LUH PUTU IDA HARINI, KARTIKA SARI

The metric dimension of connected graph G is the cardinality of minimum resolving set in graph G. In this research, we study how to find the metric dimension of lobster graph L_n (q;r). Lobster graph L_n (q;r) is a regular lobster graph with n vertices backbone on the main path, every backbone vertex is connected to q hand vertices and every hand vertex is connected to r finger vertices, with n,q,r ∈N,n≥2. We obtain the metric dimension of lobster graph L_2 (1;1) is 1, the metric dimension of lobster graph L_n (1;1) for n>2 is 2, the metric dimension of lobster graph L_n (q;1) for n≥2 and q≥2 is n(q-1) and the metric dimension of lobster graph L_n (q;r) for n≥2,q≥1 and r≥2 is nq(r-1). Keywords: lobster graph, metric dimension, resolving set.

54-58

Authors: PUTU NOPITA PURNAMA NINGSIH, KETUT JAYANEGARA, I PUTU EKA NILA KENCANA

The aim of this research is to determine the relationship between environmental, behavioral, health services, education, and economic variables to health status in the Province of Bali. These variables are constructs (latents ) that can not be measured directly by observation. If there was a relationship between latent and its indicators, it is recomended to use Structural Equation Modeling (SEM). In this research we used variance-based SEM i.e. Generalized Structured Component Analysis (GSCA). This method not based on many assumptions such as the data does not have a multivariate normal distribution, the sample size does is not necessary large. Moreover, GSCA provides by overall goodness-fit of the model. The result of this research indicates that the environmental, behavioral, economic and educational variable influenced health status, but health service does not significantly affect the health status; economic does not significantly affect the environment; and education does not significantly affect the behavior. The result of the FIT value 0.450 and the AFIT value 0.429 showed that overall model in this research is not good enough because of both of these values are under 0.50. Keywords: Latent Variable, Structural Equation Modeling (SEM), Generalized Structured Component Analysis(GSCA), Derajat Kesehatan