Deep Learning Rnn-Lstm Model For Forecasting Tourist Visits In Yogyakarta Using Bps Time-Series Data
DOI:
https://doi.org/10.52436/1.jutif.2025.6.6.5467Keywords:
Deep Learning, Long Short-Term Memory, Recurrent Neural Network, TourismAbstract
Tourism is a crucial sector in Indonesia's economic growth, particularly in Yogyakarta, contributing significantly to revenue, job creation, and infrastructure development. However, the COVID-19 pandemic has significantly impacted the tourism industry, making tourist arrival forecasting crucial for effective government policy decision-making. This study aims to predict tourist arrivals in Yogyakarta using deep learning models, specifically the Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) algorithms, chosen for their ability to process time series data and address non-linearity issues. Tourist arrival data from the Yogyakarta Central Statistics Agency (BPS) was used to train and test the model. Model evaluation was conducted using the Root Mean Squared Error (RMSE) metric to measure prediction accuracy. The results show that this model can accurately predict tourist arrival patterns, which can support strategic decision-making regarding the procurement of tourism facilities in Yogyakarta. The impact of this research is to provide practical benefits for local governments and tourism industry players in planning tourism promotion and management strategies. With more accurate predictions, relevant parties can prepare necessary resources and optimize tourism services according to projected visitor numbers.
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S. M. Al-Selwi, M. F. Hassan, S. J. Abdulkadir, A. Muneer, E. H. Sumiea, A. Alqushaibi, and M. G. Ragab, “RNN-LSTM: From applications to modeling techniques and beyond—Systematic review,” Journal of King Saud University - Computer and Information Sciences, vol. 36, no. 5, p. 102068, 2024, doi: 10.1016/j.jksuci.2024.102068.
K. Albeladi, B. Zafar, and A. Mueen, “Time Series Forecasting using LSTM and ARIMA,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 1, pp. 313–320, 2023, doi: 10.14569/IJACSA.2023.0140133.
S. Amarneni and D. R. S. Valarmathi, “Diagnosing the MRI brain tumour images through RNN-LSTM,” E-Prime - Advances in Electrical Engineering, Electronics and Energy, vol. 9, p. 100723, Nov. 2023, doi: 10.1016/j.prime.2024.100723.
D. T. Andariesta and M. Wasesa, “Machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic: a multisource Internet data approach,” Journal of Tourism Futures, pp. 1–17, 2022, doi: 10.1108/JTF-10-2021-0239.
Q. B. Baloch et al., “Impact of tourism development upon environmental sustainability: a suggested framework for sustainable ecotourism,” Environmental Science and Pollution Research, vol. 30, no. 3, pp. 5917–5930, 2023, doi: 10.1007/s11356-022-22496-w.
O. Barkan, J. Benchimol, I. Caspi, E. Cohen, A. Hammer, and N. Koenigstein, “Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks,” International Journal of Forecasting, vol. 39, no. 3, pp. 1145–1162, 2023, doi: 10.1016/j.ijforecast.2022.04.009.
S. Chaurasia, D. Dasgupta, and R. Regunathan, “T5LSTM-RNN based Text Summarization Model for Behavioral Biology Literature,” Procedia Computer Science, vol. 218, pp. 585–593, 2022, doi: 10.1016/j.procs.2023.01.040.
Y. Dai, J. Wei, and F. Qin, “Recurrent neural network (RNN) and long short-term memory neural network (LSTM) based data-driven methods for identifying cohesive zone law parameters of nickel-modified carbon nanotube reinforced sintered nano-silver adhesives,” Materials Today Communications, vol. 39, p. 108991, Jan. 2024, doi: 10.1016/j.mtcomm.2024.108991.
N. Dowlut and B. Gobin-Rahimbux, “Forecasting resort hotel tourism demand using deep learning techniques – A systematic literature review,” Heliyon, vol. 9, no. 7, p. e18385, 2023, doi: 10.1016/j.heliyon.2023.e18385.
Y. Jing, X. Ge, and B. Liu, “Toward understandable semi-supervised learning fault diagnosis of chemical processes based on long short-term memory ladder autoencoder (LSTM-LAE) and self-attention (SA),” Computers and Chemical Engineering, vol. 189, p. 108817, Jul. 2024, doi: 10.1016/j.compchemeng.2024.108817.
S. Juliana, S. O. Sihombing, S. Esther, D. M. Lemy, and A. Pramezwary, “The Role of Social Media, Brand Awareness, and Authenticity in Shaping Intentions to Visit Sawarna Tourism Village,” International Journal of Sustainable Development and Planning, vol. 18, no. 10, pp. 3253–3264, 2023, doi: 10.18280/ijsdp.181025.
K. Khosravi, F. Rezaie, J. R. Cooper, Z. Kalantari, S. Abolfathi, and J. Hatamiafkoueieh, “Soil water erosion susceptibility assessment using deep learning algorithms,” Journal of Hydrology, vol. 618, p. 129229, Jan. 2023, doi: 10.1016/j.jhydrol.2023.129229.
S. Kraus, S. Durst, J. J. Ferreira, P. Veiga, N. Kailer, and A. Weinmann, “Digital transformation in business and management research: An overview of the current status quo,” International Journal of Information Management, vol. 63, Dec. 2021, doi: 10.1016/j.ijinfomgt.2021.102466.
T. Kumail, A. Mandić, H. Li, and F. Sadiq, “Uncovering the interconnectedness of tourism growth, green technological advancements and climate change in prominent Asian tourism destinations,” Tourism Management Perspectives, vol. 53, Mar. 2023, doi: 10.1016/j.tmp.2024.101284.
P. Kumari and D. Toshniwal, “Deep learning models for solar irradiance forecasting: A comprehensive review,” Journal of Cleaner Production, vol. 318, p. 128566, Jul. 2021, doi: 10.1016/j.jclepro.2021.128566.
H. Laaroussi, F. Guerouate, and M. Sbihi, “Deep Learning Approach and Topic Modelling for Forecasting Tourist Arrivals,” International Journal of Electrical and Computer Engineering Systems, vol. 14, no. 4, pp. 411–419, 2023.
Q. Luo, S. Cai, N. Lv, and X. Fu, “Daily forecasting of tourism demand: An ST-LSTM model with social network service co-occurrence similarity,” Information and Management, vol. 62, no. 1, p. 104056, 2025, doi: 10.1016/j.im.2024.104056.
A. Q. Munir, “Spatial weighting: modification of the spatial area proximity matrix weights,” International Journal of Information Technology (Singapore), 2024, doi: 10.1007/s41870-024-01865-3.
A. Q. Munir, S. Hartati, and A. Musdholifah, “Early identification model for dengue haemorrhagic fever (DHF) outbreak areas using rule-based stratification approach,” International Journal of Intelligent Engineering and Systems, vol. 12, no. 2, pp. 246–260, 2019, doi: 10.22266/IJIES2019.0430.24.
Y. Nanjappa, V. Kumar Nassa, G. Varshney, B. Lal, S. Pandey, and A. V. Turukmane, “Improving migration forecasting for transitory foreign tourists using an Ensemble DNN-LSTM model,” Entertainment Computing, vol. 50, p. 100665, Jan. 2024, doi: 10.1016/j.entcom.2024.100665.
K. Nusair, F. Okumus, O. M. Karatepe, U. F. Alfarhan, and G. L. de Larrea, “From tourist motivations to buying decisions: A multilevel engagement perspective,” Tourism Management Perspectives, vol. 48, p. 101148, Sep. 2022, doi: 10.1016/j.tmp.2023.101148.
T. A. B. S. Pushkar, “IWF-ECTIC: Improved Wiener filtering and ensemble of classification model for tourism image classification Histogram of Oriented Gradients,” Multimedia Tools and Applications, 2024, doi: 10.1007/s11042-024-20452-z.
R. Soelaiman and M. H. Purnomo, “Implementation of recurrent neural network and boosting method for time-series forecasting,” in Proc. ICICI-BME, Dec. 2009, doi: 10.1109/ICICI-BME.2009.5417296.
P. Sugiartawan, S. Hartati, and A. Musdholifah, “Modeling of a tourism group decision support system using risk analysis based knowledge base,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 7, pp. 354–363, 2020, doi: 10.14569/IJACSA.2020.0110747.
A. Supriatna, E. Hertini, B. Subtini, D. Susanti, and S. Supian, “Prediction of Tourist Arrivals To the Island of Bali With Holt Method of Winter and Seasonal Autoregressive Integrated Moving Average (Sarima),” J. Sains Dasar, vol. 6, no. 2, pp. 81–90, 2017.
Y. Wang, Y. Zhao, and S. Addepalli, “Remaining useful life prediction using deep learning approaches: A review,” Procedia Manufacturing, vol. 49, pp. 81–88, 2020, doi: 10.1016/j.promfg.2020.06.015.
A. Witayangkurn, A. Arai, and R. Shibasaki, “Development of Big Data-Analysis Pipeline for Mobile Phone Data with Mobipack and Spatial Enhancement,” ISPRS International Journal of Geo-Information, vol. 11, no. 3, 2022, doi: 10.3390/ijgi11030196.
M. J. Yang and N. Zhu, “Online public opinion attention, digital transformation, and green investment: A deep learning model based on artificial intelligence,” Journal of Environmental Management, vol. 371, p. 123294, Oct. 2024, doi: 10.1016/j.jenvman.2024.123294.
S. Yang, Q. Li, D. Jang, and J. Kim, “Deep learning mechanism and big data in hospitality and tourism: Developing personalized restaurant recommendation model to customer decision-making,” International Journal of Hospitality Management, vol. 121, p. 103803, May 2024, doi: 10.1016/j.ijhm.2024.103803.
J. Zhang, F. Chen, Z. Cui, Y. Guo, and Y. Zhu, “Deep Learning Architecture for Short-Term Passenger Flow Forecasting in Urban Rail Transit,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 11, pp. 7004–7014, 2021, doi: 10.1109/TITS.2020.3000761.
Z. Zhang, F. Wang, and L. Deng, “Identifying node-corridor-network of tourist flow and influencing factors using GPS big data: A case study in Gansu and Qinghai provinces, China,” International Journal of Applied Earth Observation and Geoinformation, vol. 135, p. 104271, Oct. 2024, doi: 10.1016/j.jag.2024.104271.
Ayu Sofia, Dien, Z., & Erda, G. (2025). Forecasting International Tourist Arrivals to Indonesia Using LSTM: Post-Pandemic Analysis for 2024-2025. Journal of Mathematics, Computations and Statistics, 8(2), 316–324. https://doi.org/10.35580/jmathcos.v8i2.7309
Julfia, F. T., Sibuea, S., & Satryawati, E. (2025). Penerapan Algoritma Neural Network dalam Prediksi Kedatangan Wisatawan Mancanegara di DKI Jakarta Melalui Pintu Masuk Bandara Soekarno-Hatta. Joined Journal (Journal of Informatics Education), 8(1), 25. https://doi.org/10.31331/joined.v8i1.3813
Junaidi, T., Hariyadi, M. A., & Abidin, Z. (2025). OPTIMASI METODE LONG SHORT-TERM MEMORY MENGGUNAKAN SELEKSI FITUR BORUTA UNTUK MEMPREDIKSI KEDATANGAN WISATAWAN. JURNAL APLIKASI DAN INOVASI IPTEKS “SOLIDITAS” (J-SOLID), 8(1), 26–33. https://doi.org/10.31328/js.v8i1.7089
Kanjanasupawan, J., & Chen, Y. (2019). Prediction of Tourist Behaviour : Tourist Visiting Places by Adapting Convolutional Long Short-Term Deep Learning. 2019 International Conference on System Science and Engineering (ICSSE), 12–17.
Lisnawati, A., Alam, S., & Kurniawan, I. (2025). APLIKASI PREDIKSI KEDATANGAN WISATAWAN MANCANEGARA KE INDONESIA DENGAN ALGORITMA LONG SHORT-TERM MEMORY (LSTM). JATI (Jurnal Mahasiswa Teknik Informatika), 9(5), 8343–8349. https://doi.org/10.36040/jati.v9i5.15055
Luo, Q., Cai, S., Lv, N., & Fu, X. (2025). Information & Management Daily forecasting of tourism demand : An ST-LSTM model with social network service co-occurrence similarity. Information & Management, 62(1), 104056. https://doi.org/10.1016/j.im.2024.104056
Nanjappa, Y., Kumar, V., Varshney, G., Lal, B., Pandey, S., & Turukmane, A. V. (2024). Improving migration forecasting for transitory foreign tourists using an Ensemble DNN-LSTM model. Entertainment Computing, 50(November 2023), 100665. https://doi.org/10.1016/j.entcom.2024.100665
Nonik Erawati, K., Sugiartawan, P., Nengah, N., Ardriani, D., Dewa, I., Bayu, A., Hartama, M., Ngurah, G., Frasetya, H., Gede, I., Mahendra, O., Erawati, K. N., Sugiartawan, P., & Ardriani, N. N. D. (2025). LSTM Neural Network for Predicting Tourist Arrivals to Bali. Jurnal Sistem Informasi Dan Komputer Terapan Indonesia (JSIKTI), 8(1), 81–90. https://infoteks.org/journals/index.php/jsikti/article/view/211
Song, M., & Abdullah, M. I. (2025). Application of LSTM in the dynamic changes of tourism market in Hunan Province. Proceedings of 2025 2nd International Conference on Digital Economy, Blockchain and Artificial Intelligence, DEBAI 2025, 36–40. https://doi.org/10.1145/3762249.3762256.
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