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All Journal Journal of Mathematics and Applied Statistics Prosiding Seminar Nasional CORISINDO
Afnidia, Tria
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Computer Science & IT Mathematics

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Prediction of Domestic and Foreign Tourist Visits Using the Long Short-Term Memory (LSTM) Method Afnidia, Tria; Herlina Putri, Novi; Soraya, Siti; Firmansyah, Firmansyah
Journal of Mathematics and Applied Statistics Vol. 3 No. 1 (2025): June 2025
Publisher : Yayasan Insan Literasi Cendekia (INLIC) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/mathstat.v3i1.286

Abstract

The tourism sector makes an important contribution to supporting regional economic growth. Among the various provinces in Indonesia, West Nusa Tenggara (NTB) stands out as one of the main tourist destinations that has shown a fairly rapid increase in the number of tourist visits in recent years. This study uses Adam optimization and gradient clipping techniques to predict domestic and foreign tourist visits in NTB using the Long Short-Term Memory (LSTM) method. Monthly historical data for the period 2014–2023 from the NTB Tourism Office was processed through Min-Max Scaling normalization and divided with a ratio of 70:30 and 80:20. The LSTM model with a 4-layer architecture (2 LSTM layers with 50 units and 2 Dense layers) was tested using the Root Mean Squared Error (RMSE) metric. Based on the results obtained, the best configuration was shown at a ratio of 70:30 with 200 epochs, producing the lowest RMSE of 66.70 on the training data and 33.24 on the testing data. This implies that the model can capture seasonal patterns and visit trends, although it is less responsive to outliers such as natural disasters. This implementation provides a basis for tourism capacity planning and data-based destination management.
Prediksi Kunjungan Wisatawan Nusantara dan Mancanegara Menggunakan Metode Long Short Term Memory (LSTM) Putri, Novi Herlina; Afnidia, Tria
CORISINDO 2025 Vol. 1 (2025): Prosiding Seminar Nasional CORISINDO 2025
Publisher : CORISINDO 2025

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/corisindo.v1.5307

Abstract

Sektor pariwisata memiliki kontribusi penting dalam menunjang pertumbuhan ekonomi daerah. Di antara berbagai provinsi di Indonesia, Nusa Tenggara Barat (NTB) tampil sebagai salah satu tujuan wisata utama yang menunjukkan peningkatan cukup pesat dalam jumlah kunjungan wisatawan dalam beberapa tahun terakhir. Penelitian ini bertujuan untuk memprediksi kunjungan wisatawan nusantara dan mancanegara di NTB menggunakan metode Long Short-Term Memory (LSTM) dengan optimasi Adam dan teknik gradient clipping. Data historis bulanan periode 2014–2023 dari Dinas Pariwisata NTB diproses melalui normalisasi Min-Max Scaling dan dibagi dengan rasio 70:30 dan 80:20. Model LSTM dengan arsitektur 4 lapisan (2 lapisan LSTM berunit 50 dan 2 lapisan Dense) diuji menggunakan metrik Root Mean Squared Error (RMSE). Berdasarkan hasil yang diperoleh menunjukkan konfigurasi terbaik pada rasio 70:30 dengan 200 epoch, menghasilkan RMSE terendah sebesar 66.70 pada data training dan 33,24 pada data testing. Hal ini berimpilkasi bahwa model mampu menangkap pola musiman dan tren kunjungan, meskipun kurang responsif terhadap outlier seperti bencana alam. Implementasi ini memberikan dasar untuk perencanaan kapasitas pariwisata dan manajemen destinasi berbasis data.
Co-Authors Firmansyah Firmansyah Herlina Putri, Novi Putri, Novi Herlina Siti Soraya