The purpose of this study is to discuss and develop Spatial-Temporal Autologistic Regression Model (STARM) to represent spreading of the Aedes aegypti which is indicated by the endemic level of DHF (Dengue Hemorrhagic Fever) in East Java. The method which is used to estimate STARM parameter is Bayesian method with Markov Chain Monte Carlo (MCMC) and Gibbs Sampler simulation. This study observed 38 districts as spatial lattice units, meanwhile temporal unit is represented by monthly period of evidence (January-December) in 2002-2008. Result of the research was obtained STARM model that indicate the spreading pattern of the Aedes aegypti that is indicated by the endemic level of DHF incidence in East Java have spatially and temporally positive correlation. Model validation using 95% credible interval shows that all estimators are significant. This is also supported by a MAE value 0.09 and the percentage of correctly classified predicted data 90%, which means there are 90 correctly classified data of 100 prediction data.
Leadership is a process of influencing, directing or giving an example of employees in order to achieve the objectives of the organization and is a key element in the effectiveness of the organization. In addition to the style of leadership, the success of an organization or company in achieving its objectives can also be influenced by the commitment of the organization. Where organizational commitment is a commitment created by each individual for the betterment of the organization. The purpose of this research is to obtain a model of leadership style and organizational commitment to job satisfaction and employee performance, and determine the factors that influence job satisfaction and employee performance using SEM with Bayesian approach. This research was conducted at Statistics FNI employees in Malang, with 15 people. The result of this study showed that the measurement model, all significant indicators measure each latent variable. Meanwhile in the structural model, it was concluded there are a significant difference between the variables of Leadership Style and Organizational Commitment toward Job Satisfaction directly as well as a significant difference between Job Satisfaction on Employee Performance. As for the influence of Leadership Style and variable Organizational Commitment on Employee Performance directly declared insignificant.
The polynomial regression model is extended the multiple linear regression. The selection of Bayesian polynomial regression model with INLA required Criterion. Criterion is using the measure fit model with the available data. There are three criteria, namely DIC, WAIC and CPO. The smaller criterion value from DIC, WAIC and CPO on a model show the best Bayesian polynomial regression model with INLA.
This study aims to analyze the relationship between macroeconomic variables in Indonesia, namely GDP with money supply, exchange rate of rupiah to US Dollar, exports, imports and interest rates. The background problem is to analyze the best method to influence government targets or policies on economic growth by studying the relationship of macroeconomic variables. Previous studies analyzing the relationship between macroeconomic variables in Indonesia have used multiple linear regression analysis. Using VECM analysis we can find out the short-term and long-term effects on the relationship between macroeconomic variables in Indonesia. The analysis used in this study is the Vector Error Correction Model with Maximum Likelihood estimation. Based on the result, the cointegration test found that there is a long-term relationship. Based on the VECM model (3), in the short term there is a relationship between macroeconomic variables and in the long run there is a long-term causality relationship in the GDP and export models. It is expected that the Government and the Central Bank will work together cooperatively in making policies to keep control of the money supply, exchange rate of rupiah to US Dollar and interest rates to enable to stimulate the economy.
This research contains to hybrid Context Based Clustering Method integrated with Fuzzy Geographically Weighted Clustering-Particle Swarm Optimization (CFGWC-PSO). The computation time using CFGWC-PSO Algorithm is faster than other algorithms. CFGWC-PSO algorithm was applied on 11 variables from data factors causing the spread of dengue in East Java. One of the parameters used in this analysis is fuzziness (m), which is the parameter used to measure the level of obscurity from the clustering results. In this paper will use different fuzziness (m) values to evaluating best fuzziness value (m) which are appropriate used to clustering with CFGWC-PSO algorithm. CFGWC-PSO algorithm using fuzziness (m) = 1.5 and fuzziness (m) = 2, number of clusters = 2 then CFGWC-PSO will evaluated using IFV index. Based on IFV index found that the best clustering in this case with CFGWC-PSO algorithm is with using fuzziness value (m) = 2.
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