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All Journal EIGEN MATHEMATICS JOURNAL Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika
Kumala, Mei Dita
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Mathematics

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Analisis Peramalan Jumlah Kunjungan Wisatawan Domestik Menggunakan Model Long Short-Term Memory (LSTM) di Kota Pangkal Pinang Hidayat, Muhammad Irfan; Kumala, Mei Dita
Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika Vol. 9 No. 1 (2026): Volume 9 Nomor 1 Tahun 2026
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/proximal.v9i1.7478

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.
Peramalan Jumlah Perjalanan Wisatawan di Kota Pangkal Pinang: ARIMA vs. LSTM vs. Prophet” Hidayat, Muhammad Irfan; Kumala, Mei Dita
Eigen Mathematics Journal Vol 9 No 1 (2026): June
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v9i1.345

Abstract

Accurate forecasting of tourism demand is crucial for regional economic planning, yet existing studies often rely on univariate time-series models or single forecasting techniques without considering spatial interdependence among regions. This study proposes a correlation-based comparative forecasting framework to evaluate the performance of ARIMA, Long Short-Term Memory (LSTM), and Prophet in predicting domestic tourist trips in Pangkal Pinang City, Indonesia. Monthly data from January 2019 to July 2025 were obtained from Statistics Indonesia, and highly correlated neighboring regions were systematically selected using a heatmap correlation analysis to enhance model input relevance. After applying Min–Max normalization and an 80:20 train–test split, all models were evaluated using Root Mean Squared Error (RMSE) under identical experimental settings. The results indicate that Prophet consistently achieves the lowest RMSE, demonstrating superior capability in capturing non-linear dynamics, seasonal variability, and abrupt structural changes in tourism demand compared to ARIMA and LSTM. These findings provide empirical evidence that decomposable time-series models with automatic trend and seasonality handling offer distinct advantages over both classical statistical and deep learning approaches in medium-term tourism forecasting. The proposed framework contributes a concise, data-efficient, and replicable methodology that supports evidence-based tourism planning and strategic decision-making.
Co-Authors Hidayat, Muhammad Irfan