Aplikasi Analitik Data untuk Memprediksi Perilaku Konsumen Perjalanan Pascapandemi
Tinjauan Komprehensif Pendekatan Pembelajaran Mesin
Kata Kunci:
analitik data, perilaku konsumen, pariwisata pascapandemi, pembelajaran mesin, XGBoost, big data, transformasi digital, analitik prediktif, pemulihan pariwisataAbstrak
Penelitian ini mengkaji pemanfaatan strategis analitik data dan metodologi pembelajaran mesin untuk memprakirakan pola perilaku konsumen perjalanan di era pascapandemi, dengan penekanan khusus pada pergeseran preferensi yang sedang berkembang menuju pertimbangan keamanan, fleksibilitas, dan keberlanjutan. Penelitian menggunakan metodologi tinjauan literatur sistematis yang mensintesis publikasi dari jurnal terindeks Scopus (Q1/Q2) dan terbitan terakreditasi Sinta 1–2 periode 2021–2025, dilengkapi data industri dari UN Tourism dan lembaga riset pasar terkemuka. Hasil analisis menunjukkan bahwa pariwisata internasional mencapai pemulihan hampir penuh pada 2024 dengan 1,4 miliar kedatangan (99% dari tingkat prapandemi) dan penerimaan sebesar USD 1,9 triliun; algoritma pembelajaran mesin seperti XGBoost, Random Forest, dan model Deep Learning menunjukkan efektivitas yang substansial dalam memprediksi pola perilaku wisatawan dengan tingkat akurasi melebihi 85%; serta analitik big data melalui Google Trends dan platform ulasan digital memungkinkan pemantauan real-time evolusi preferensi wisatawan, khususnya terkait protokol kesehatan, fleksibilitas pemesanan, dan pilihan pariwisata berkelanjutan. Penelitian ini berkontribusi dengan menyajikan kerangka analitis yang terintegrasi dan rekomendasi strategis berbasis bukti bagi agen perjalanan Indonesia dalam menavigasi imperatif transformasi digital.
Kata kunci: analitik data; perilaku konsumen; pariwisata pascapandemi; pembelajaran mesin; XGBoost; big data; transformasi digital; analitik prediktif; pemulihan pariwisata
Referensi
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