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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Konvergensi Research In Management and Accounting (RIMA) Journal of Information System,Graphics, Hospitality and Technology Jurnal Informatika dan Sistem Informasi Jurnal Teknik Informatika (JUTIF) Teknika Jurnal AbdiMas Nusa Mandiri Insyst : Journal of Intelligent System and Computation JUSTIN (Jurnal Sistem dan Teknologi Informasi) Joutica : Journal of Informatic Unisla Jurnal Nasional Teknik Elektro dan Teknologi Informasi Reflection Journal Jurnal Sistem Cerdas dan Rekayasa Jurnal Sistem Informasi Research In Management and Accounting (RIMA)
Devi Dwi Purwanto
Institut Sains Dan Teknologi Terpadu Surabaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Mathematics Physics Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

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Empirical Evaluation of IndoBERT and LSTM for Sentiment Analysis of Tourism Reviews: A Data-Driven Study on Kenjeran Park Purwanto, Devi Dwi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.4901

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.
Co-Authors . Gunawan . Gunawan . Gunawan . Gunawan Aditya Dwi Aryanto Agustinus Bimo Gumelar Aji Prasetya Wibawa Budianto, Herman Endang Setyati Eric Sugiharto Honggara Gabeler, Robertus Geraldyn Alexandro Hadikusuma, Jeremiah Irawan Hakkun Elmunsyah Hartarto Junaedi Herman Budianto Herman Thuan To Saurik Hongary, Kevin - Ikhsan, Teguh Pribadi Indra Maryati Jimmy Tegarianto Joan Santoso Kartika Salim, Shierly Kevin - Hongary Kristian, Yosi Limantoro, Stephen Ekaputra Mochammad Faid Nathaniel Tjoa Novi Duwi Setyorini Setya Ardhi Sitepu, Rasional Siti Sendari Slamet Winardi Stefanie Angelina Gunarto Stephen Ekaputra Limantoro Subandoro, Philipus Suryo Suhatati Tjandra Tristianti, Berliana Az Zahra Yosi Kristian Yunan Kholilul Fatah