An In-depth Exploration of Sentiment Analysis on Hasanuddin Airport using Machine Learning Approaches

An In-depth Exploration of Sentiment Analysis on Hasanuddin Airport using Machine Learning Approaches

Sentiment Analysis; Support Vector Machine; Naive Bayes; K-Nearest Neighbor; SMOTE; Sultan Hasanuddin Airport

Machine learning-based sentiment analysis has become essential for understanding public perceptions of public services, including air transportation. Sultan Hasanuddin Airport, one of the main gateways in eastern Indonesia, faces the challenge of improvingservices amid changing user needs due to the COVID-19 pandemic. This study aims to compare the effectiveness of three machine learning algorithms-SupportVector Machine (SVM), Naive Bayes Multinomial, and K-Nearest Neighbor (KNN)-in analyzing the sentiment of user reviews related to airport services. The research also explores data splitting techniques, text preprocessing, data balancing using SMOTE, model validation, and method parameterization to ensure optimal results. The review data was retrieved from Google Maps (2021-2024) and underwent manual labelling. Text preprocessing includes normalization, stemming using Sastrawi, and stopword removal. The data-balancingtechnique uses SMOTE, while model evaluation is done with stratified k-fold cross-validation. SVM with a linear kernel showed the best performance, achieving an F1-score of 98.4%. Naive Bayes performed optimally, achieving an F1-score of 93.9%, while KNN recorded the best F1-score of 92.0%. SMOTE was shown to improve Naive Bayes'performance on unbalanced datasets, although it did not significantly impact SVM. The findings of this study provide data-driven recommendations to improve services at Sultan Hasanuddin Airport, such as the management of cleaning facilities, waiting room comfort, and passenger flow efficiency. In addition, this research opens up opportunities for developing real-time sentiment analysis systems that can be applied in other air transportation sectors

Lilis Nur Hayati1, Fitrah Yusti Randana2*,Herdianti Darwis3

https://jurnal.iaii.or.id/index.php/RESTI/article/view/6253/1036

Department of Information System, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

08-03-2025

FAJAR BAGUS W

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