BibTex Citation Data :
@article{J.Gauss19348, author = {Sasmita Sari and Tarno Tarno and Diah Safitri}, title = {PEMILIHAN INPUT MODEL REGRESSION ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (RANFIS) UNTUK KAJIAN DATA IHSG}, journal = {Jurnal Gaussian}, volume = {6}, number = {3}, year = {2017}, keywords = {}, abstract = { The Jakarta Composite Index (JCI) is one of indexes issued by the Indonesia Stock Exchange (IDX) with its calculation component using all the registered emiten. Several factors affecting the JCI are Dow Jones Index, inflation, and USD/IDR exchange rate. The study used Regression Adaptive Neuro Fuzzy Inference System (RANFIS) to analyze the affect of predictor variables on the JCI. The role of regression in RANFIS is a preprocessing in the determination of input in ANFIS. The optimum ANFIS model in RANFIS is strongly influenced by three things, they are input determination, membership functions, and rule. The technique of defining rules followed the rule of genfis1 and genfis3. The model accuracy was measured using the smallest RMSE and MAPE. Based on the empirical studies which implemented Dow Jones Index, inflation, and USD/IDR exchange rate as the predictors and JCI as the response, it was obtained that optimum RANFIS model with gauss membership function, the number of cluster 2 with 2 rules generated by genfis3 produced RMSE in-sample 233.0 and out-sample 301.9, as well as MAPE in-sample 6.5% and out-sample 4.8%. While in regression analysis, it obtained RMSE in-sample 351.27 and out-sample 590.99, as well as MAPE in-sample 9.6% and out-sample 10.2% with violation of assumption. This shows that the result of RANFIS method is better than regression analysis. Keywords : JCI, regression analysis, neuro fuzzy, RANFIS, genfis }, issn = {2339-2541}, pages = {449--458} doi = {10.14710/j.gauss.6.3.449-458}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/19348} }
Refworks Citation Data :
The Jakarta Composite Index (JCI) is one of indexes issued by the Indonesia Stock Exchange (IDX) with its calculation component using all the registered emiten. Several factors affecting the JCI are Dow Jones Index, inflation, and USD/IDR exchange rate. The study used Regression Adaptive Neuro Fuzzy Inference System (RANFIS) to analyze the affect of predictor variables on the JCI. The role of regression in RANFIS is a preprocessing in the determination of input in ANFIS. The optimum ANFIS model in RANFIS is strongly influenced by three things, they are input determination, membership functions, and rule. The technique of defining rules followed the rule of genfis1 and genfis3. The model accuracy was measured using the smallest RMSE and MAPE. Based on the empirical studies which implemented Dow Jones Index, inflation, and USD/IDR exchange rate as the predictors and JCI as the response, it was obtained that optimum RANFIS model with gauss membership function, the number of cluster 2 with 2 rules generated by genfis3 produced RMSE in-sample 233.0 and out-sample 301.9, as well as MAPE in-sample 6.5% and out-sample 4.8%. While in regression analysis, it obtained RMSE in-sample 351.27 and out-sample 590.99, as well as MAPE in-sample 9.6% and out-sample 10.2% with violation of assumption. This shows that the result of RANFIS method is better than regression analysis.
Keywords: JCI, regression analysis, neuro fuzzy, RANFIS, genfis
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