[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125291-en":3,"doc-seo-125291-105":29,"detail-sidebar-cat-0-en-105":90},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},125291,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Optimizing Sentiment Analysis for Lombok Tourism Using SMOTE and Chi-Square with Machine Learning","Tourism is a vital economic sector for Lombok Island, and rapid growth increases the need to understand tourists’ perceptions and sentiments for better service quality. Online review sentiment analysis supports identifying strengths and weaknesses, informing marketing and service improvements. Text classification faces challenges including noise, class imbalance, and high-dimensional features. This study optimizes tweet sentiment classification using SMOTE oversampling and Chi-Square feature selection with SVM and Naïve Bayes, improving accuracy substantially without hyperparameter tuning.","JURNAL RESTI  \n(Rekayasa Sistem dan Teknologi Informasi)  \nVol. 9 No. 4 (2025) 706-713 e-ISSN: 2580-0760  \n\n| Optimizing Sentiment Analysis for Lombok Tourism Using SMOTE and\u003Cbr>Chi-Square with Machine Learning\u003Cbr>Hairani Hairani1*, Anthony Anggrawan2, Muhammad Ridho Akbar3, Khasnur Hidjah4, Muhammad Innuddin5\u003Cbr>1 3 4 5Department of Computer Science, Faculty of Engineering, Universitas Bumigora, Mataram, Indonesia\u003Cbr>2Department of Information Technology Education, Faculty of Education, Universitas Bumigora, Mataram, Indonesia\u003Cbr>[1](1 hairani@universitasbumigora.ac.id)[ hairani@universitasbumigora.ac.id](1 hairani@universitasbumigora.ac.id) , [2](2anthony.anggrawan@universitasbumigora.ac.id)[anthony.anggrawan@universitasbumigora.ac.id](2anthony.anggrawan@universitasbumigora.ac.id) ,\u003Cbr>[3](3 2001010152@universitasbumigora.ac.id)[ 2001010152@universitasbumigora.ac.id](3 2001010152@universitasbumigora.ac.id), [4](4 khasnur72.h@universitasbumigora.ac.id)[ khasnur72.h@universitasbumigora.ac.id](4 khasnur72.h@universitasbumigora.ac.id), [5](5 inn@universitasbumigora.ac.id)[ inn@universitasbumigora.ac.id](5 inn@universitasbumigora.ac.id)\u003Cbr>Abstract\u003Cbr>Tourism is a vital economic sector for Lombok Island, which is renowned for its natural beauty and cultural richness as a top destination. The rapid growth of tourism in Lombok requires a deep understanding of tourists'perceptions and sentiments to ensure an optimal service quality. The sentiment analysis of online reviews is valuable for identifying service strengths and weaknesses and addressing tourists' needs more effectively. This not only enhances tourist satisfaction, but also aids in the design of more effective marketing strategies. However, text data analysis from online reviews presents unique challenges such as noise, class imbalance, and numerous features that may affect classification results. Therefore, this study aims to classify tourist sentiment toward Lombok tourism using machine learning methods combined with feature selection and oversampling techniques. This study focuses on optimizing sentiment analysis of tourism-related tweets using a combination of SMOTE oversampling and Chi-Square feature selection on improving classification performance without hyperparameter tuning. The study applies machine learning methods, such as SVM and Naïve Bayes, with feature selection and oversampling using ChiSquare and SMOTE. The dataset used was sentiment data regarding Lombok tourism obtainedfrom Twitter in 2023, consisting of 940 instances divided into three classes: Negative, Neutral, and Positive. The research findings show that the use of SMOTE and Chi-Square can improve the accuracy of the SVM and Naive Bayes methods. Without optimization, the SVM method achieved an accuracy of 73.93% and a Naive Bayes of 67.02%. After optimization with SMOTE and Chi-Square, the accuracy increased for SVM by 90% and Naive Bayes by 84% to classify tourist sentiment towards Lombok tourism. The implications indicate that combining data balancing using SMOTE with feature selection via Chi-Square effectively improves the performance of sentiment classification models for tourist opinions on Lombok's tourism.\u003Cbr>Keywords: chi-square feature selection; optimization of classification methods; SMOTE oversampling; tourism sentiment |  |\n| --- | --- |\n| How to Cite: Hairani, Anggrawan, A., Muhammad Ridho Akbar, Khasnur Hidjah, & Muhammad Innuddin. (2025) . Optimizing Sentiment Analysis for Lombok Tourism Using SMOTE and Chi-Square with Machine Learning. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 9(4), 706-713. [https://doi.org/10.29207/resti.v9i4.6623](https://doi.org/10.29207/resti.v9i4.6623)\u003Cbr>Permalink/DOI: [https://doi.org/10.29207/resti.v9i4.6623](https://doi.org/10.29207/resti.v9i4.6623) |  |\n| Received: May 4, 2025\u003Cbr>Accepted: June 23, 2025\u003Cbr>Available Online: July 13, 2025 | This is an open-access article under the CC BY 4.0 License Published by Ikatan Ahli Informatika Indo","cbCaiuSJtDE2cZAk","https://ap.wps.com/l/cbCaiuSJtDE2cZAk","pdf",454855,1,"English","en",105,"# Introduction\n## Sentiment analysis for tourism\n## Challenges in online text data\n## Related work","[{\"question\":\"What problem does the study address in Lombok tourism sentiment analysis?\",\"answer\":\"The study targets accurate classification of tourist sentiment from online reviews/tweets, which is needed to improve service quality and marketing strategies.\"},{\"question\":\"Which techniques are used to optimize classification performance?\",\"answer\":\"The method combines SMOTE oversampling to handle class imbalance with Chi-Square feature selection to reduce/choose relevant features.\"},{\"question\":\"How do the optimized results compare with the baseline models?\",\"answer\":\"Without optimization, SVM reaches 73.93% accuracy and Naïve Bayes 67.02%; with SMOTE and Chi-Square, accuracy rises to about 90% for SVM and 84% for Naïve Bayes.\"}]","Optimizing Sentiment Analysis for Lombok Tourism Using SMOTE and Chi-Square with Machine Learning | PDF",1785897999,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"optimizing-sentiment-analysis-for-lombok-tourism-using-smote-and-chi-square-with-machine-learning","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/optimizing-sentiment-analysis-for-lombok-tourism-using-smote-and-chi-square-with-machine-learning/125291/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does the study address in Lombok tourism sentiment analysis?","Question",{"text":74,"@type":75},"The study targets accurate classification of tourist sentiment from online reviews/tweets, which is needed to improve service quality and marketing strategies.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which techniques are used to optimize classification performance?",{"text":79,"@type":75},"The method combines SMOTE oversampling to handle class imbalance with Chi-Square feature selection to reduce/choose relevant features.",{"name":81,"@type":72,"acceptedAnswer":82},"How do the optimized results compare with the baseline models?",{"text":83,"@type":75},"Without optimization, SVM reaches 73.93% accuracy and Naïve Bayes 67.02%; 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