Analisis Peramalan Jumlah Kunjungan Wisatawan Domestik Menggunakan Model Long Short-Term Memory (LSTM) di Kota Pangkal Pinang

Authors

  • Muhammad Irfan Hidayat Universitas Bangka Belitung
  • Mei Dita Kumala Bangka Belitung, Indonesia

DOI:

https://doi.org/10.30605/proximal.v9i1.7478

Keywords:

Peramalan, Long Short-Term Memory (LSTM), Jaringan Saraf Tiruan, Root Mean Square Error (RMSE)

Abstract

Sektor pariwisata berperan penting sebagai pendorong utama pertumbuhan ekonomi suatu daerah, dengan jumlah wisatawan menjadi indikator yang patut diperhatikan. Peningkatan kunjungan wisatawan berimplikasi pada meningkatnya potensi risiko, sehingga diperlukan upaya peramalan yang tepat. Dalam penelitian ini, metode Long Short-Term Memory (LSTM) diterapkan untuk memprediksi jumlah perjalanan wisatawan domestik di Kota Pangkal Pinang dan dibandingkan dengan metode ARMA serta SARIMA. Hasil perbandingan menunjukkan bahwa metode LSTM menghasilkan nilai RMSE sebesar 32.431,219, yang lebih rendah dibandingkan ARMA (41.273,347) dan SARIMA (101.884,554). Hasil studi menunjukkan bahwa LSTM memiliki performa prediksi yang lebih efektif, sehingga metode ini lebih direkomendasikan.

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Published

2026-01-10

How to Cite

Hidayat, M. I., & Kumala, M. D. (2026). Analisis Peramalan Jumlah Kunjungan Wisatawan Domestik Menggunakan Model Long Short-Term Memory (LSTM) di Kota Pangkal Pinang . Proximal: Jurnal Penelitian Matematika Dan Pendidikan Matematika, 9(1), 1–12. https://doi.org/10.30605/proximal.v9i1.7478

Issue

Vol. 9 No. 1 (2026): Volume 9 Nomor 1 Tahun 2026

Section

Articles

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Copyright (c) 2026 Muhammad Irfan Hidayat, Mei Dita Kumala

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