Prediksi Jumlah Kunjungan Wisatawan Nusantara ke Sulawesi Tenggara Menggunakan Pendekatan Machine Learning
DOI:
https://doi.org/10.51454/decode.v5i3.1352Keywords:
LSTM, Peramalan Pariwisata, Prediksi Derer Waktu, Sulawesi Tenggara, SVRAbstract
Prediksi jumlah kunjungan wisatawan merupakan aspek penting dalam perencanaan dan pengambilan kebijakan di sektor pariwisata. Penelitian ini bertujuan untuk menganalisis perbandingan kinerja model Support Vector Regression (SVR) dan Long Short-Term Memory (LSTM) dalam memprediksi jumlah kunjungan wisatawan nusantara ke Provinsi Sulawesi Tenggara. Data yang digunakan diperoleh dari Badan Pusat Statistik (BPS) Provinsi Sulawesi Tenggara periode Januari 2018 hingga Desember 2023. Model SVR dan LSTM masing-masing diuji menggunakan metrik Root Mean Square Error (RMSE) dan Mean Absolute Error (MAE) untuk mengukur tingkat akurasi hasil prediksi. Hasil penelitian menunjukkan bahwa model SVR menghasilkan nilai RMSE dan MAE yang lebih rendah pada 13 dari 17 kabupaten/kota, sehingga memiliki tingkat akurasi yang lebih baik dibandingkan model LSTM. Sementara itu, LSTM cenderung lebih sensitif terhadap fluktuasi data yang ekstrem pascapandemi, sehingga menghasilkan prediksi yang kurang stabil. Berdasarkan hasil tersebut, dapat disimpulkan bahwa model SVR lebih sesuai digunakan untuk memprediksi jumlah kunjungan wisatawan di wilayah dengan karakteristik data yang bersifat musiman dan fluktuatif seperti Sulawesi Tenggara. Temuan ini diharapkan dapat menjadi dasar dalam pengembangan sistem prediksi pariwisata berbasis machine learning yang lebih akurat dan adaptif.
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