[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124186-en":3,"doc-seo-124186-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124186,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","An In-depth Exploration of Sentiment Analysis on Hasanuddin Airport using Machine Learning Approaches - Article","Machine learning-based sentiment analysis supports understanding public perceptions of services, including air transportation. Sultan Hasanuddin Airport in eastern Indonesia faces service improvement needs as user expectations evolve during and after the COVID-19 pandemic. This study compares three algorithms—SVM, Naive Bayes Multinomial, and KNN—on sentiment classification of user reviews using structured preprocessing, SMOTE balancing, and stratified k-fold cross-validation. SVM (linear kernel) achieves the highest F1-score at 98.4%, while Naive Bayes reaches 93.9% and KNN 92.0%. SMOTE boosts Naive Bayes on unbalanced data, guiding data-driven service recommendations and enabling real-time sentiment systems for other air sectors.","Accredited SINTA 2 Ranking  \nDecree of the Director General of Higher Education, Research, and Technology, No. 158/E/KPT/2021 Validity period from Volume 5 Number 2 of 2021 to Volume 10 Number 1 of 2026  \nPublished online at: [http://jurnal.iaii.or.id](http://jurnal.iaii.or.id)  \nJURNAL RESTI  \n(Rekayasa Sistem dan Teknologi Informasi)  \nVol. 9 No. 2 (2025) 195-208 e-ISSN: 2580-0760  \nAn In-depth Exploration of Sentiment Analysis on Hasanuddin Airport using Machine Learning Approaches  \nLilis Nur Hayati1, Fitrah Yusti Randana 2*, Herdianti Darwis3  \n1Department of Information System, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia  \n2, 3Department of Informatics, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia  \n[1](1 lilis.nurhayati@umi.ac.id)[ lilis.nurhayati@umi.ac.id](1 lilis.nurhayati@umi.ac.id), [2](2 13020210286@umi.ac.id)[ 13020210286@umi.ac.id](2 13020210286@umi.ac.id), [3](3 herdianti.darwis@umi.ac.id)[ herdianti.darwis@umi.ac.id](3 herdianti.darwis@umi.ac.id)  \nAbstract  \nMachine 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 improving services amid changing user needs due to the COVID-19 pandemic. This study aims to compare the effectiveness of three machine learning algorithms-Support Vector 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-balancing technique 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.  \nKeywords: Sentiment Analysis; Support Vector Machine; Naive Bayes; K-Nearest Neighbor; SMOTE; Sultan Hasanuddin Airport.  \nHow to Cite: Fitrah Yusti Randana, Lilis Nur Hayati, and H. Darwis,“An In-depth Exploration of Sentiment Analysis on Hasanuddin Airport using Machine Learning Approaches”, J. RESTI (Rekayasa Sist. Teknol. Inf.), vol. 9, no. 2, pp. 195-208, Mar. 2025.  \nDOI: [https://doi.org/10.29207/resti.v9i2.6253](https://doi.org/10.29207/resti.v9i2.6253)  \n1. Introduction  \nIndonesia, with more than 17,000 islands stretching from Sabang to Merauke, makes air transportation the primary means to support the mobility of people and tourists [1] . The speed and efficiency of air transportation are important pillars in driving national economic growth, trade, and tourism. Sultan Hasanuddin Airport, as one of the main gateways in eastern Indonesia, has a strategic role in connecting these regions [2] . The quality of services and facilities at the airport is a crucial factor that affects the experience of service users, which is reflected in customer reviews on cleanliness, comfort, efficiency of passenger flow, and staff attitude [3], [4] . With the COVID-19 pandemic, airport management faces  \nsig","cbCailJAoKNCgCRw","https://ap.wps.com/l/cbCailJAoKNCgCRw","pdf",1245753,1,14,"English","en",105,"# Introduction\n## Research gap and objectives\n# Methodology\n## Data collection and labeling\n## Text preprocessing and balancing\n## Model validation and parameterization\n# Results and discussion\n## Algorithm comparison\n## Effect of SMOTE and preprocessing\n# Conclusion\n## Practical recommendations and future work","[{\"question\":\"Which machine learning algorithms are compared for sentiment analysis of Hasanuddin Airport reviews?\",\"answer\":\"The study compares Support Vector Machine (SVM), Naive Bayes Multinomial, and K-Nearest Neighbor (KNN) for analyzing the sentiment of user reviews related to airport services.\"},{\"question\":\"What data preparation techniques are used before model training?\",\"answer\":\"Text preprocessing includes normalization, Sastrawi stemming, and stopword removal. The dataset is also balanced using SMOTE, and evaluation uses stratified k-fold cross-validation.\"},{\"question\":\"Which algorithm performs best and what is the main reported metric?\",\"answer\":\"SVM with a linear kernel performs best, achieving an F1-score of 98.4%. Naive Bayes reaches 93.9%, while KNN records 92.0%.\"}]","An In-depth Exploration of Sentiment Analysis on Hasanuddin Airport using Machine Learning Approaches - Article | PDF",1785820918,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"an-in-depth-exploration-of-sentiment-analysis-on-hasanuddin-airport-using-machine-learning-approaches-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/an-in-depth-exploration-of-sentiment-analysis-on-hasanuddin-airport-using-machine-learning-approaches-article/124186/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning algorithms are compared for sentiment analysis of Hasanuddin Airport reviews?","Question",{"text":75,"@type":76},"The study compares Support Vector Machine (SVM), Naive Bayes Multinomial, and K-Nearest Neighbor (KNN) for analyzing the sentiment of user reviews related to airport services.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data preparation techniques are used before model training?",{"text":80,"@type":76},"Text preprocessing includes normalization, Sastrawi stemming, and stopword removal. The dataset is also balanced using SMOTE, and evaluation uses stratified k-fold cross-validation.",{"name":82,"@type":73,"acceptedAnswer":83},"Which algorithm performs best and what is the main reported metric?",{"text":84,"@type":76},"SVM with a linear kernel performs best, achieving an F1-score of 98.4%. 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