The model of local wisdom for smart wellness tourism with optimization multilayer perceptron

Jatmika, Sunu, Patmanthara, Syaad, Wibawa Aji, Prasetya and Kurniawan, Fachrul ORCID: https://orcid.org/0000-0002-3709-8764 (2024) The model of local wisdom for smart wellness tourism with optimization multilayer perceptron. Journal of Theoretical and Applied Information Technology, 102 (2). pp. 640-652. ISSN 1817-3195

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Abstract

This study focuses on the influence of variations in the number of hidden layers in the Artificial Neural Network (ANN) method on model performance and interpretability of results. The method applied involves integrating local wisdom to optimize the Artificial Neural Network (ANN) model. This approach combines locally relevant aspects with a conceptual framework to improve ANN performance. Evaluation of the results involves the performance metrics, MSE, MAE, RMSE, and F2 Score to find the best-hidden layer pattern in the Artificial Neural Network (ANN) model. The test results are based on a dataset with five indicators totaling 30 input layers and tested on the Multi Layer Perceptron (MLP) model. The results of testing a dataset with 30 input layers divided into 5 indicators produced performance metrics MSE 0.01346, MAE 0.09740, and RMSE 0.12094. The concept with a 16-hidden layer model pattern has high complexity and produces better predictions with fewer errors. Additionally, hidden layer 11 performs well, displaying a solid capacity to describe the variance in target data with an R2_Score of 0.17374. This produces two groups of ANN test results: the first group with improved accuracy (MSE, MAE, RMSE), and the second group highlights the optimal performance of hidden layers 16 and 11 (R2 Score). Local wisdom contributes to smart wellness.

Item Type:	Journal Article
Keywords:	artificial neural network; local wisdom integration; performance metrics; hidden layer;patterns; multi-layer perceptron
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems 08 INFORMATION AND COMPUTING SCIENCES > 0899 Other Information and Computing Sciences > 089999 Information and Computing Sciences not elsewhere classified 09 ENGINEERING > 0999 Other Engineering
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Fachrul Kurniawan
Date Deposited:	24 Apr 2024 13:14

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