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Published Jan 30, 2023
https://doi.org/10.51717/simkom.v8i1.103
Author
Muhammad Ali Ridla
Fakultas Sains dan Teknologi, Universitas Ibrahimy
Nur Azise
Fakultas Sains dan Teknologi, Universitas Ibrahimy
Miftahur Rahman
Fakultas Teknik, Universitas Muhammadiyah Jember
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Vol 8 No 1 (2023): Vol 8 No 1 - 2023

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Perbandingan Model Time Series Forecasting Dalam Memprediksi Jumlah Kedatangan Wisatawan Dan Penumpang Airport

Abstract

Kemajuan dunia pariwisata dalam kehidupan zaman sekarang sudah sangat lazim ditemui di setiap negara di dunia. Meningkatkan kualitas pariwisata merupakan hal yang sangat penting bagi setiap negara, mengingat pariwisata merupakan salah satu sumber pemasukan negara. Oleh karena itu, salah satu parameter yang sangat penting akan hal ini adalah mengetahui jumlah pengunjung atau wisatawan setiap waktu, serta dapat memanfaatkan data historis yang ada untuk memprediksi jumlah wisatawan di waktu yang akan datang. Pada penelitian ini akan dilakukan prediksi/forecasting jumlah wisatawan dan penumpang di airport menggunakan metode Seasonal Auto Regressive Integrated Moving Average (SARIMA), Long-short Term Memory (LSTM), dan Prophet pada dua dataset time series dengan frekuensi bulanan. Dari tiga model forecasting tersebut, diperoleh hasil masing-masing lalu dikomparasi, model SARIMA merupakan model yang paling baik performanya dengan nilai RMSE dan MSE yang paling kecil.

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Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite
[1]
M. A. Ridla, N. Azise, and M. Rahman, “Perbandingan Model Time Series Forecasting Dalam Memprediksi Jumlah Kedatangan Wisatawan Dan Penumpang Airport”, simkom, vol. 8, no. 1, pp. 1-14, Jan. 2023.

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