ABSTRACT
COMPARISON OF DECISION TREE, NAIVE BAYES AND SUPPORT
VECTOR MACHINE ALGORITHMS USING TOURISM DATA
By
Bagas Febrianto
Informatics Engineering
Tourism in Indonesia is an important sector with various destinations. In 2022, the
number of foreign tourists reached 5.9 million and increased to 8.51 million in
2023, with Ngurah Rai airport as the main entry point. To maximize tourism
potential, understanding tourist preferences based on personal characteristics such
as Gender and occupation is essential. Data analytics techniques such as Decision
Tree, Naive Bayes, and Support Vector Machine (SVM) are useful for identifying
these preference patterns. The use of software such as Orange Data mining helps
researchers process data efficiently. Research shows that Orange Data mining is
effective in analyzing traveler preference data. After a 5-time comparison with
different split data, the results show that Decision Tree has the best accuracy,
precision, and Recall with Confusion Matrix, while SVM excels in Cross Validation.
Keywords: Decision Tree; Naïve Bayes; Support Vector Machine.