Forecasting the Number of Domestic Tourists Based on Destination City Using the Support Vector Machine (SVM) Method in Bali Province

DOI

10.2991/978-94-6463-882-0_11How to use a DOI?

Keywords

Domestic Tourists; Forecasting; SVM

Abstract

Tourism plays a crucial role in Bali’s economy, making accurate forecasts of domestic tourist arrivals essential for effective planning and resource allocation. This study aims to forecast the number of domestic tourists based on destination cities in Bali Province using the Support Vector Machine (SVM) method. The dataset consists of historical records of domestic tourist arrivals categorized by destination cities over several years. The research methodology includes data preprocessing, model development using SVM, and model performance evaluation using the Mean Absolute Percentage Error (MAPE). The results show that the predictive model performs well in several areas, with low MAPE values for Buleleng (0.0233), Klungkung (0.0413), Karangasem (0.0487), and Badung (0.072). However, higher MAPE values were observed for Bangli (9.7632) and Jembrana (2.245), indicating lower prediction accuracy in these regions. These findings highlight the potential of SVM for forecasting domestic tourist arrivals and provide valuable insights to support data-driven tourism management strategies in Bali Province.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - I Putu Bagus Arya Pradnyana
AU  - I Putu Oka Wisnawa
AU  - I Komang Wiratama
PY  - 2025
DA  - 2025/11/14
TI  - Forecasting the Number of Domestic Tourists Based on Destination City Using the Support Vector Machine (SVM) Method in Bali Province
BT  - Proceedings of the International Conference on Sustainable Green Tourism Applied Science - Social Applied Science 2025 (ICOSTAS-SAS 2025)
PB  - Atlantis Press
SP  - 84
EP  - 90
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-94-6463-882-0_11
DO  - 10.2991/978-94-6463-882-0_11
ID  - Pradnyana2025
ER  -