Empirical Evaluation of IndoBERT and LSTM for Sentiment Analysis of Tourism Reviews: A Data-Driven Study on Kenjeran Park

Devi Dwi Purwanto

doi:10.52436/1.jutif.2026.7.1.4901

Authors

Devi Dwi Purwanto Department of Informatics, Widya Mandala Surabaya Catholic University, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.1.4901

Keywords:

Deep Learning, IndoBERT, LSTM, Sentiment Analysis, Urban Coastal Park, Tourism

Abstract

Tourism plays a pivotal role in Indonesia’s economic and cultural landscape, contributing significantly to job creation, regional development, and international recognition. This study evaluates the performance of IndoBERT, a state-of-the-art Indonesian language model, and Long Short-Term Memory (LSTM) networks for sentiment classification of 2,560 Google reviews of Kenjeran Park in Surabaya, consisting of 54% positive, 28% neutral, and 18% negative sentiments. Preprocessing steps included slang replacement, stemming, stopword removal, and tokenization, with class imbalance addressed through weighted loss adjustments. IndoBERT was fine-tuned using contextual embeddings with a learning rate of 0.00005, while the LSTM model employed a 128-unit architecture trained over 150 epochs with the Adam optimizer. Experimental results show that IndoBERT achieved 87.50% accuracy, 0.7697 precision, 0.7643 recall, and 0.7643 F1-score, outperforming LSTM’s 77.93% accuracy, 0.6826 precision, 0.6812 recall, and 0.6826 F1-score. This research establishes a comparative benchmark of transformer-based and RNN-based architectures for Indonesian tourism review sentiment analysis, introduces a domain-specific preprocessing pipeline with imbalance handling, and provides actionable insights for digital tourism analytics. Beyond its technical contributions, the study highlights the urgency of advancing robust natural language processing approaches for low-resource languages, thereby strengthening the field of informatics and supporting data-driven decision-making in the tourism sector.

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