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Komparasi model support vector machine dan backpropagation dalam peramalan jumlah wisatawan mancanegara di provinsi Bali

Comparison of support vector machine and backpropagation models in forecasting the number of foreign tourists in Bali province

Department of Information Technology, Universitas Udayana, Bukit Jimbaran, Badung, Bali, Indonesia 80361, Indonesia

Received: 28 Jul 2020; Revised: 7 Dec 2020; Accepted: 26 Feb 2021; Published: 30 Apr 2021; Available online: 20 Apr 2021.
Open Access Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
Tourism in Bali is one of the major industries which play an important role in developing the global economy in Indonesia. Good forecasting of tourist arrival, especially from foreign countries, is needed to predict the number of tourists based on past information to minimize the prediction error rate. This study compares the performance of SVM and Backpropagation to find the model with the best prediction algorithm using data from foreign tourists in Bali Province. The results of this study recommend the best forecasting using the SVM model with the radial kernel function. The best accuracy of the SVM model obtained the lowest error values of MSE 0.0009, MAE 0.0186, and MAPE 0.0276, compared to Backpropagation which obtained MSE 0.0170, MAE 0.1066, and MAPE 0.1539.
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Keywords: tourism; prediction; number of tourist; SVM; kernel; Backpropagation
Funding: Universitas Udayana

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Last update: 2021-07-23 04:06:21

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Last update: 2021-07-23 04:06:23

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