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Asian Institute of Research, Journal Publication, Journal Academics, Education Journal, Asian Institute
Asian Institute of Research, Journal Publication, Journal Academics, Education Journal, Asian Institute

Engineering and Technology Quarterly Reviews

ISSN 2622-9374

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open access

Published: 26 May 2023

Forecasting Tourist Arrivals with Partial Time Series Data Using Long-Short Term Memory (LSTM)

Harun Mukhtar, Muhammad Akmal Remli, Khairul Nizar Syazwan Wan Salihin Wong, Evans Fuad, Julaiha Siregar, Yoze Rizki

Universitas Muhammadiyah Riau (Indonesia), Universiti Malaysia Kelantan (Malaysia)

journal of social and political sciences
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Pages: 56-64

Keywords: Tourism, Forecast, LSTM, Neural Network, Time Series


Tourism is a source of foreign exchange income, especially in the economic field. Increasing foreign tourist arrivals is essential in supporting the economy of Indonesia. The development of the tourism industry can be seen from the increase in tourist visits every year. Based on data obtained by the Indonesian Central Statistics Agency (BPS), there was an increase and decrease in the number of tourist visits during 2006-2019. Along with these conditions, the provision of various tourism products and services needed to support the industry must be adjusted to prevent financial losses. Unfortunately, tourism products are generally easily damaged, so it is necessary to forecast tourist arrivals. This study aims to predict the arrival of foreign tourists to Indonesia using the Long Short Term Memory (LSTM) method. This method is suitable for sequential data such as tourist arrival data. This shows the results of the evaluation using the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) of 591897.46 for RMSE and 636.2 for MAPE. Based on this research, it can be concluded that LSTM is suitable to be used as a model to predict the arrival of foreign tourists in Indonesia.


  1. Abidin, N. H. Z., Remli, M. A., Mohd Ali, N., Eh Phon, D. N., Yusoff, N., Adli, H. K., & Busalim, A. H. (2020). Improving Intelligent Personality Prediction using Myers-Briggs Type Indicator and Random Forest Classifier. International Journal of Advanced Computer Science and Applications, 11(11), 192–199.

  2. Bahi, Y. F. El, Ezzine, L., El Moussami, H., & Aman, Z. (2018). Modeling and Forecasting of Fuel Selling Price Using Time Series Approach: Case Study. 2018 5th International Conference on Control, Decision and Information Technologies, CoDIT 2018, 283–288.

  3. Bandara, K., Shi, P., Bergmeir, C., Hewamalage, H., Tran, Q., & Seaman, B. (2019). Sales demand forecast in E-commerce using a long short-term memory neural network methodology. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11955 LNCS, 462–474.

  4. Bouktif, S., Fiaz, A., Ouni, A., & Serhani, M. A. (2018). Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches. Energies, 11(7).

  5. Bulchand-gidumal, J. (2020). Impact of Artificial Intelligence in Travel, Tourism, and Hospitality. Handbook of E-Tourism, August.

  6. Fattah, J., Ezzine, L., Aman, Z., El Moussami, H., & Lachhab, A. (2018). Forecasting of demand using ARIMA model. International Journal of Engineering Business Management, 10, 1–9.

  7. Goyal A., Kumar R., Kulkarni S., K. S. & V. M. (2016). A Solution to Forecast Demand Using Long Short-Term Memory Recurrent Neural Networks for Time Series Forecasting. Midwest Decision …, 18.

  8. Haviluddin, & Jawahir, A. (2015). Comparing of ARIMA and RBFNN for short-term forecasting. International Journal of Advances in Intelligent Informatics, 1(1), 15–22.

  9. Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9.

  10. Hsieh, S.-C. (2021). Tourism Demand Forecasting Based on an LSTM Network and Its Variants. Algorithms, 14(8), 243.

  11. Hsu, C. J., & Chen, H. H. (2020). Taxi demand prediction based on LSTM with residuals and multi-head attention. VEHITS 2020 - Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems, Vehits, 268–275.

  12. Huang, Y., Xu, C., Ji, M., Xiang, W., & He, D. (2020). Medical service demand forecasting using a hybrid model based on ARIMA and self-adaptive filtering method. BMC Medical Informatics and Decision Making, 20(1), 1–14.

  13. Kang, T., Lim, D. Y., Tayara, H., & Chong, K. T. (2020). Forecasting of Power Demands using Deep Learning. Applied Sciences (Switzerland), 10(20), 1–11.

  14. Kilimci, Z. H., Akyuz, A. O., Uysal, M., Akyokus, S., Uysal, M. O., Atak Bulbul, B., Ekmis, M. A., & Silva, T. C. (2019). An improved demand forecasting model using deep learning approach and proposed decision integration strategy for supply chain. Complexity, 2019.

  15. Lee, K., Kang, D. Y., Choi, H. R., Park, B. K., Cho, M. J., & Kim, D. (2018). Intermittent Demand Forecasting with a Recurrent Neural Network Model Using IoT Data. International Journal of Control and Automation, 11(3), 153–168.

  16. Mariyono, J. (2017). Determinants of Demand for Foreign Tourism in Indonesia. Jurnal Ekonomi Pembangunan, 18(1), 82.

  17. Masri, F., Saepudin, D., & Adytia, D. (2020). Forecasting of Sea Level Time Series using Deep Learning RNN, LSTM, and BiLSTM, Case Study in Jakarta Bay, Indonesia. E-Proceeding of Engineering, 7(2), 8544–8551.

  18. Mazlan, A. U., Sahabudin, N. A., Remli, M. A., Ismail, N. S. N., Mohamad, M. S., Nies, H. W., & Abd Warif, N. B. (2021). A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data. Processes, 9(8), 1466.

  19. Mukhtar, H., Taufiq, R. M., Herwinanda, I., Winarso, D., & Hayami, R. (2022). Forecasting Covid-19 Time Series Data using the Long Short-Term Memory ( LSTM ). International Journal of Advanced Computer Science and Applications, 13(10), 211–217.

  20. Ouhame, S., & Hadi, Y. (2019). Multivariate workload prediction using Vector Autoregressive and Stacked LSTM models. ACM International Conference Proceeding Series.

  21. Pal, S., Ma, L., Zhang, Y., & Coates, M. (2021). RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting. ArXiv.

  22. Peng, L., Wang, L., Ai, X. Y., & Zeng, Y. R. (2021). Forecasting Tourist Arrivals via Random Forest and Long Short-term Memory. Cognitive Computation, 13(1), 125–138.

  23. Quy, T. Le, B, W. N., Spiliopoulou, M., & Ntoutsi, E. (2020). A Neighborhood-Augmented LSTM Model for Taxi-Passenger Demand. Semantic, 1, 100–116.

  24. Ren, X., Li, Y., Zhao, J. J., & Qiang, Y. (2021). Tourism Growth Prediction Based on Deep Learning Approach. Complexity, 2021.

  25. Rice, W. L., Park, S. Y., Pan, B., & Newman, P. (2019). Forecasting campground demand in US national parks. Annals of Tourism Research, 75(March), 424–438.

  26. Román-Portabales, A., López-Nores, M., & Pazos-Arias, J. J. (2021). Systematic review of electricity demand forecast using ann-based machine learning algorithms. Sensors, 21(13), 1–23.

  27. Santoso, A., Pranata, R., Wibowo, A., Al-Farabi, M. J., Huang, I., & Antariksa, B. (2021). Cardiac injury is associated with mortality and critically ill pneumonia in COVID-19: A meta-analysis. American Journal of Emergency Medicine, 44(xxxx), 352–357.

  28. Sehovac, L., Nesen, C., & Grolinger, K. (2019). Forecasting building energy consumption with deep learning: A sequence to sequence approach. Proceedings - 2019 IEEE International Congress on Internet of Things, ICIOT 2019 - Part of the 2019 IEEE World Congress on Services, 108–116.

  29. Weytjens, H., Lohmann, E., & Kleinsteuber, M. (2019). Cash flow prediction: MLP and LSTM compared to ARIMA and Prophet. Electronic Commerce Research, 0123456789.

  30. Yang, Y., & Wong, K. K. F. (2012). A Spatial Econometric Approach to Model Spillover Effects in Tourism Flows. Journal of Travel Research, 51(6), 768–778.

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