Analysis of Domestic Tourist Forecasts to Bali Based on Destination City Using the LSTM Method

DOI

10.2991/978-94-6239-805-4_4How to use a DOI?

Keywords

Domestic tourists; Forecasting; LSTM; MAPE; City

Abstract

The recovery of Bali’s tourism post-COVID-19 urgently requires spatial forecasting of domestic tourists due to the disparities between regencies/cities. The objective of this research is to predict the monthly number of domestic tourist visits to each regency/municipality in Bali from 2019 to 2025 using the Long Short-Term Memory (LSTM) deep learning method to support data-driven tourism policies. Model evaluation is comprehensively conducted using Mean Absolute Percentage Error (MAPE). The data is sourced from official publications by BPS Bali with a monthly time resolution per district. Based on testing the LSTM model across nine regions in Bali, the model demonstrates a good level of accuracy in five regions, achieving an average MAPE below 20%. Among these, Jembrana exhibits the best performance with a MAPE of 17.25%. Bangli shows a significant mathematical anomaly with a MAPE of 185.61% due to exceptionally low actual baseline values during specific periods. Overall, the developed LSTM model provides valuable spatial insights for tailored tourism mitigation and regional planning in Bali.

Copyright

© 2026 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.

Download article (PDF)

TY  - CONF
AU  - I Putu Bagus Arya Pradnyana
AU  - I Putu Oka Wisnawa
AU  - I Nyoman Rai Widartha Kesuma
PY  - 2026
DA  - 2026/10/08
TI  - Analysis of Domestic Tourist Forecasts to Bali Based on Destination City Using the LSTM Method
BT  - Proceedings of the International Conference on Sustainable Green Tourism Applied Science - Engineering Applied Science 2026 (ICOSTAS-EAS 2026)
PB  - Atlantis Press
SP  - 24
EP  - 32
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-805-4_4
DO  - 10.2991/978-94-6239-805-4_4
ID  - Pradnyana2026
ER  -