An Improvement of Parameter Estimation Accuracy of Structural Equation Modeling using Hybridization of Artificial Neural Network in the Entrepreneurship Structural Model

被引：0

作者：

Devianto, Dodi ^{[1
]}

Wulandari, Frilianda ^{[1
]}

Yanuar, Ferra ^{[1
]}

Rahmi, Izzati ^{[1
]}

Yollanda, Mutia ^{[1
]}

机构：

[1] Department of Mathematics and Data Science, Faculty of Mathematics and Natural Sciences, Andalas University, Padang,25163, Indonesia

来源：

Applied Mathematics and Nonlinear Sciences | 2023年 / 8卷 / 02期

关键词：

In developing optimal entrepreneurship; several variables such as motivation; knowledge; intensity; and capacity are required to determine their relationship using the Partial Least Square-Structural Equation Modeling (PLS-SEM). The results show that entrepreneurial motivation and knowledge significantly affect intensity. Also; motivation and intensity significantly influenced capacity. The parameter estimator of PLS-SEM can be improved by applying hybridization to the Artificial Neural Network (PLS-ANN) using the 2:32:8:1 architecture in which motivation and intensity were the input while capacity was the output. The comparison parameter accuracy model measured by MSE; RMSE; and MAE shows the improvement accuracy by PLS-ANN better than PLS-SEM. © 2023 Dodi Devianto et al; published by Sciendo;

D O I：

10.2478/amns.2023.1.00411

中图分类号：

学科分类号：

摘要：

引用

页码：2279 / 2302

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