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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Optimalisasi Jurnal Mekanova : Mekanikal, Inovasi dan Teknologi Al-Mal:Jurnal Akuntansi dan Keuangan Islam Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Industri dan Inovasi (INVASI) Jurnal Pengabdian Bakti Akademisi
Adib Adib, Adib
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Transportation

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Tourism Marketing Intelligence: Machine Learning for Demand Forecasting Using Google Trends in Indonesia Ghofar, Khidhir Akbar; Hidayat, Muhammad Alif; Adib, Adib
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2628

Abstract

Tourism demand forecasting remains challenging due to the dynamic nature of consumer behavior and the lag in traditional economic indicators. This study investigates the predictive power of online search behavior captured through Google Trends in forecasting international tourist arrivals to Indonesia, integrating digital footprints with macroeconomic indicators using machine learning approaches. Monthly data spanning January 2011 to December 2024 (N=168) were collected from multiple sources: tourist arrivals from Statistics Indonesia, Google Trends composite index from 11 destination-related keywords, exchange rates, and consumer price index. Three machine learning algorithms Random Forest, XGBoost, and LSTM were compared using temporal train-test split (80:20). Random Forest demonstrated superior performance with Test MAPE of 14.12% and R² of 0.811, outperforming XGBoost (MAPE 21.41%) and LSTM (MAPE 24.56%). Feature importance analysis revealed that Google Trends emerged as the second most important predictor (12.22% contribution), surpassing all economic indicators combined (7.20%), validating its role as a leading indicator of tourism demand. This study contributes methodologically by providing empirical evidence for the value of integrating digital behavioral signals with traditional predictors in emerging destination contexts, and practically by demonstrating deployment-ready forecasting accuracy for tourism stakeholders in Indonesia.
Co-Authors Abdiel Khaleil Akmal Arrazy Elba Ridha Azhar Azhar azwanda, Azwanda Danil Danil Fajar Okta Widarta Fajri Hadi Ghofar, Khidhir Akbar Hartati, Rita Hidayat, Muhammad Alif Iing Pamungkas Irawan, Heri Tri Jayadi, Farid Khairul Hadi Mahmud Basuki Masykur, Masykur Muzakir, Muzakir Muzammil, Abdul Rita Hartati Rizki Agam Syahputra Sofiyanurriyanti Sugeng Santoso T. M. Azis Pandria Tripoli, Bambang