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All Journal Toba: Journal of Tourism, Hospitality, and Destination
Adelina Rahmawati
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Social Sciences

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Peran Media Sosial Instagram dalam Memprediksi Jumlah Wisatawan Nusantara (Studi Kasus: Labuan Bajo, Nusa Tenggara Timur) Navika Artiari; Erna Nurmawati; Adelina Rahmawati; Teguh Sugiyarto
TOBA: Journal of Tourism, Hospitality, and Destination Vol. 5 No. 2 (2026): May 2026
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/toba.v5i2.7577

Abstract

The use of big data derived from social media platforms such as Instagram has increasingly been adopted as an alternative data source in tourism analysis, particularly to understand destination promotion patterns and online audience engagement. This study aims to identify the main topics of tourism promotional content on social media and to examine their contribution to predicting the number of domestic tourist trips to the Province of East Nusa Tenggara. Topic modeling was conducted using the Latent Dirichlet Allocation (LDA) method on Instagram captions posted by tourism stakeholders in East Nusa Tenggara. The results of the topic modeling revealed three main topics, namely marine tourism attractiveness, tour package and sailing activity offerings, and trip and boat rental promotions. The analysis of engagement rates (ER) indicates that the average monthly ER of tourism-related accounts in East Nusa Tenggara reached 28.07. Among the identified topics, the marine tourism attractiveness topic recorded the highest average ER, at 42.24, indicating strong audience interest in visual content and narratives related to marine natural attractions. Furthermore, the prediction of domestic tourist trips was carried out by integrating macroeconomic variables, tourism indicators, and social media–based variables. The evaluation results demonstrate that the Long Short-Term Memory (LSTM) model incorporating all explanatory variables—including hotel occupancy rates, inflation, transportation price indices, the COVID-19 pandemic variable, account-level ER, and topic-specific ER—achieved the best performance. This model produced a Mean Absolute Percentage Error (MAPE) of 18.75 percent, a Mean Absolute Error (MAE) of 83,577.40, and a Root Mean Square Error (RMSE) of 111,630.97.
Co-Authors Erna Nurmawati Navika Artiari Teguh Sugiyarto