Classification of The Level of Public Satisfaction With the Use of Water Tourism Jetski in Balai Ujung Tanjung Using the Naïve Bayes Algorithm

Nursalimah Isnaina Fatwa; Rakhmat Kurniawan

doi:10.24014/ijaidm.v7i2.32761

Classification of The Level of Public Satisfaction With the Use of Water Tourism Jetski in Balai Ujung Tanjung Using the Naïve Bayes Algorithm

Nursalimah Isnaina Fatwa, Rakhmat Kurniawan

Abstract

Jetski water tourism is one of the attractions that is often visited by the public compared to other attractions. One of the factors causing this is because there is no fee charged to visitors. The source of funds used in this tourist attraction is from the local government budget. Be it in terms of assessment to improve facilities, or even comments on whether the Jetski Water Tourism facility is good or bad. Certainly, with the public comments, it will help the government in improving its services to the community, especially in the management of this water tourism Jetski.The sentiment data collected from visitors to this Water Tourism Jetski can be used as a benchmark for the government in improving this Water Tourism Jetski facility. Both in terms of scope and the Jetski media used. By knowing the responses and comments of the community regarding Jetski Wisata Air, the government can evaluate in order to support visitor satisfaction and so that Jetski Wisata Air can last long and compete with other tourist attractions. The Naïve Bayes Algorithm has often been used in a study in the form of sentiment analysis. The Naïve Bayes model shows that the level of public satisfaction with Jetski Water Tourism in Ujung Tanjung Hall, Tanjungbalai City can be predicted with an accuracy of 75%. This indicates that the model is quite effective in identifying the level of user satisfaction, although there is a 25% possibility of inaccuracy in prediction. With this accuracy, the model can provide useful insights for the evaluation and improvement of jetski tourism services, but it should be considered to conduct further analysis to improve accuracy and get a more comprehensive picture of community satisfaction

Keywords

Classification; Data Mining; Machine Learning; Naïve Bayes Algorithm; Python

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DOI: http://dx.doi.org/10.24014/ijaidm.v7i2.32761

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