DOI of the published article https://doi.org/10.36040/jati.v10i2.17721
Implementation of the Facebook Prophet Model in Predicting the Number of Educational Tourism Visitors
Implementasi Model Facebook Prophet dalam Meramalkan Jumlah Pengunjung Wisata Edukasi
DOI:
https://doi.org/10.21070/ups.10712Keywords:
Forecasting, Educational Tour, Facebook Prophet, Extra RegressorAbstract
Kampung Lali Gadget (KLG) experiences extreme fluctuations in visitor numbers, reaching 98% between peak and lowest months, which complicates operational planning. This study applies the Facebook Prophet model to forecast visitor numbers as a decision-support tool. The model is optimized by incorporating extra regressors such as national holidays, academic holidays, and local events, along with hyperparameter tuning using Grid Search and manual fine-tuning. The best model achieved a MAPE of 20.93% (good category). Forecast results indicate a peak of 1,940 visitors in January 2026 and a low of 36 visitors in April 2026. These findings support more effective planning of curriculum, resources, and facility management.
Downloads
References
L. Anggraini and S. Chodidjah, “Pengaruh Wisata Edukasi, Niat Perilaku, Dan Lokasi Terhadap Niat Berkunjung Ulang Ke Taman Ismail Marzuki Jakarta,” Hum. Cap. Dev., vol. 10, no. 2, pp. 1–17, 2023, [Online]. Available: https://ejournal.asaindo.ac.id/index.php/hcd/article/view/2468%0Ahttps://ejournal.asaindo.ac.id/index.php/hcd/article/download/2468/1764
Kampung Lali Gadget, “Tentang Kami,” kampunglaligadget.org. Accessed: Jan. 30, 2026. [Online]. Available: https://kampunglaligadget.org/
S. J. Taylor and B. Letham, “Forecasting at Scale,” Am. Stat., vol. 72, no. 1, pp. 37–45, 2018, doi: 10.1080/00031305.2017.1380080.
C. B. Aditya Satrio, W. Darmawan, B. U. Nadia, and N. Hanafiah, “Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET,” Procedia Comput. Sci., vol. 179, no. 2020, pp. 524–532, 2021, doi: 10.1016/j.procs.2021.01.036.
F. B. Prakoso, G. Darmawan, and A. Bachrudin, “PENERAPAN METODE FACEBOOK PROPHET UNTUK MERAMALKAN,” pp. 133–147, 2023.
E. R. Putri et al., “Penerapan Algoritma Prophet Facebook untuk Memprediksi Jumlah Calon Mahasiswa Baru,” vol. 5, no. 4, pp. 1588–1596, 2024.
H. Yuliansyah, “Peramalan Jumlah Pengunjung Wisata Edukasi Museum Menggunakan Kombinasi Moving Average Dan Model Prophet Program Studi Informatika , Universitas Ahmad Dahlan , Indonesia Forecasting the Number of Visitors to Educational Tourism Museums Using a Combination,” vol. 5, no. 7, pp. 2033–2054, 2025.
M. A. Ridla, N. Azise, and M. Rahman, “PERBANDINGAN MODEL TIME SERIES FORECASTING DALAM MEMPREDIKSI JUMLAH KEDATANGAN WISATAWAN DAN PENUMPANG AIRPORT,” vol. 8, no. 1, pp. 1–14, 2023.
D. Rizkya, H. Roosaputri, and C. Dewi, “Perbandingan Algoritma ARIMA, Prophet, dan LSTM dalam Prediksi Penjualan Tiket Wisata Taman Hiburan (Studi Kasus: Saloka Theme Park),” J. Penerapan Sist. Inf. (Komputer Manajemen), vol. 4, no. 3, pp. 507–517, 2023.
S. Haben, M. Voss, and W. Holderbaum, “Core Concepts and Methods in Load Forecasting: With Applications in Distribution Networks,” Core Concepts Methods Load Forecast. With Appl. Distrib. Networks, pp. 1–331, 2023, doi: 10.1007/978-3-031-27852-5.[11]S. Kwarteng and P. Andreevich, “Comparative Analysis of ARIMA, SARIMA and Prophet Model in Forecasting,” Res. Dev., vol. 5, no. 4, pp. 110–120, 2024, doi: 10.11648/j.rd.20240504.13.
A. W. Anggraeni, A. S. Fitrani, and A. Eviyanti, “Penerapan Algoritma Support Vector Machine untuk Memprediksi Tingkat Partisipasi Pemilu terhadap Kualitas Pendidikan,” Edumatic J. Pendidik. Inform., vol. 8, no. 1, pp. 21–27, 2024, doi: 10.29408/edumatic.v8i1.24838.
T. Toharudin, R. S. Pontoh, R. E. Caraka, S. Zahroh, Y. Lee, and R. C. Chen, “Employing long short-term memory and Facebook prophet model in air temperature forecasting,” Commun. Stat. Simul. Comput., vol. 52, no. 2, pp. 279–290, 2023, doi: 10.1080/03610918.2020.1854302.
E. P. RAMDHANI and H. A. NUGROHO, “Prediksi Tegangan Catu Daya Automatic Rain Gauge berdasarkan Seasonality Algoritma Prophet,” ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., vol. 12, no. 1, p. 80, 2024, doi: 10.26760/elkomika.v12i1.80.
J. A Ilemobayo et al., “Hyperparameter Tuning in Machine Learning: A Comprehensive Review,” J. Eng. Res. Reports, vol. 26, no. 6, pp. 388–395, 2024, doi: 10.9734/jerr/2024/v26i61188.
Downloads
Additional Files
Posted
Categories
License
Copyright (c) 2026 UMSIDA Preprints Server
This work is licensed under a Creative Commons Attribution 4.0 International License.
