Abstract: Tourism is among the significant source of income to Malaysia and Taman Negara Pahang is one of the Malaysia's tourism spots and the heritage of Malaysia in achieving the Sustainable Development Goals (SDG). It has attracted many international and local tourists for its richness in flora and fauna. Currently, the information of tourists’ visits is not properly analyzed. This study integrates the internal and public information to analyze the visits. The regression models used are multiple linear regression, support vector regression, and decision tree regression to predict the tourism demand for Taman Negara, Malaysia and the best model was deployed. Predictive analytics can support the decision-making process for tourism destinations management. When the management gets a head-up of the demand in the future, they can choose a strategic planning and be more aware about the factors influencing tourism demand, such as the tourists’ web search engine behaviors for accommodation, facilities, and attractions. The factors affecting the tourism demand are determined as the first objective. The role of independent variable was set to the total number of visitors, subsequently being set as the target variable in the modeling process. A total of 30 models were generated by tuning the cross-validation parameters. This study concluded that the best model is the multiple linear regression due to lower root mean square error (RSME) value.

Sofianita Mutalib, Athila Hasya Razali, Siti Nur Kamaliah Kamarudin, Shamimi A Halim and Shuzlina Abdul-Rahman, “Prediction of Tourist Visit in Taman Negara Pahang, Malaysia using Regression Models” International Journal of Advanced Computer Science and Applications(IJACSA), 12(12), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121292

@article{Mutalib2021,
title = {Prediction of Tourist Visit in Taman Negara Pahang, Malaysia using Regression Models},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0121292},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0121292},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {12},
author = {Sofianita Mutalib and Athila Hasya Razali and Siti Nur Kamaliah Kamarudin and Shamimi A Halim and Shuzlina Abdul-Rahman}
}