PERFORMANCE COMPARISON OF SENTIMENT ANALYSIS ON NATIONAL MONUMENT (MONAS) REVIEWS USING BERT, SUPPORT VECTOR MACHINE, LOGISTIC REGRESSION, AND MULTINOMIAL NAÏVE BAYES
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Riki Daniel Tanebeth(1*)
Universitas Nusa Mandiri
(*) Corresponding Author
Abstract
The National Monument (Monas), as an icon of Indonesian tourism, faces challenges in maintaining visitor satisfaction in the digital era. Online reviews on Google Maps serve as a crucial data source for understanding public perception. However, the large volume of data and the informal nature of review language hinder manual analysis. This study aims to analyze Monas visitor sentiment and compare the performance of conventional Machine Learning models with modern Deep Learning approaches. The method involves comparing the Multinomial Naïve Bayes algorithm using TF-IDF feature extraction against the IndoBERT (Bidirectional Encoder Representations from Transformers) model based on fine-tuning. The dataset consists of 1,110 visitor reviews from the 2023-2024 period. Experimental results demonstrate that the IndoBERT model significantly outperforms Naïve Bayes, achieving an accuracy of 93.5% and an F1-Score of 93.0%, while Naïve Bayes only reached 49.1% accuracy. Further aspect-based analysis reveals that although positive sentiment is dominant (49%), there are critical complaints regarding the digital ticketing system and elevator queues. This study recommends the implementation of transformer-based models for analyzing Indonesian tourism reviews and suggests improvements in queue management for Monas management.
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