[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122098-en":3,"doc-seo-122098-105":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":4,"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},122098,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Comparison Of Machine Learning Algorithms In Public Sentiment Analysis Of TAPERA Policy","Rapid advances in information technology have reshaped public communication and opinion expression about government policies, including Indonesia’s People’s Housing Savings (Tapera) program. Social media platforms such as Twitter generate large volumes of text that can be analyzed to classify public sentiment. The study compares Naïve Bayes, Support Vector Machine, and Random Forest for sentiment classification using Twitter comment data. Accuracy results show Naïve Bayes achieves 69.17%, followed by SVM at 68.42% and Random Forest at 66.17%, indicating Naïve Bayes as the most effective choice for complex social-media language. The findings support better algorithm selection to help government understand and respond to public perceptions.","Comparison Of Machine Learning Algorithms In Public Sentiment Analysis Of TAPERA Policy  \nEklesia Sihombing1*, Muhammad Halmi Dar2, Fitri Aini Nasution3  \n1,2,3 Faculty of Science and Technology, Universitas Labuhanbatu, Sumatera Utara Indonesia  \n*Corresponding Author:  \nEmail: [eklesia.hombing@gmail.com](eklesia.hombing@gmail.com)  \nAbstract.  \nThe rapid development of information technology has changed the way people interact and express their opinions on public policies, including the People's Housing Savings (Tapera) policy in Indonesia. People now primarily express their views openly on social media platforms like Twitter, generating a substantial amount of text data for analysis to understand public sentiment. However, the main challenge in this sentiment analysis is determining the most effective machine learning algorithm for classifying public opinion with high accuracy. This study aims to compare the performance of three machine learning algorithms, namely Naïve Bayes, Support Vector Machine, and Random Forest, in analyzing public sentiment towards the Tapera policy. This study analyzes public comment data obtained from Twitter. We measure the accuracy of each algorithm to determine its optimal performance in sentiment classification. The research method consists of several stages, starting with data collection, text preprocessing to clean and prepare data, and then applying the three algorithms to analyze sentiment. The results showed that Naïve Bayes had the highest accuracy of 69. 17%, followed by Support Vector Machine with an accuracy of 68.42%, and Random Forest with an accuracy of 66. 17%. This shows that Naïve Bayes is the most effective algorithm to use in sentiment analysis of public comments related to the Tapera policy, especially in the context of complex text data from social media. The conclusion of this study is that Naïve Bayes is superior in classifying public sentiment towards the Tapera policy compared to Support Vector Machine and Random Forest. As a result, this study makes a significant contribution to selecting the most appropriate machine learning algorithm for public sentiment analysis towards public policy, which in turn can help the government understand and respond to public perceptions more effectively.  \nKeywords: Machine Learning, Naïve Bayes, Random Forest, and Sentiment Analysis Support Vector Machine and Tapera.  \nI. INTRODUCTION  \nSentiment analysis in social media has become increasingly important in understanding public opinion and behavior [1], [2] . Social media data such as Facebook, Twitter, and Instagram can provide valuable insights for businesses and researchers in understanding consumer preferences, market trends, and competitor strategies [3] . It is important for businesses to use the right data analytics tools, take action on the insights gained, and pay attention to their privacy and data security policies [4] . Sentiment analysis can help identify positive, negative, or neutral attitudes in text such as reviews, comments , and social media posts [5],[6], [7] . Sentiment analysis can also offer recommendations for product enhancement, aid in the planning of a business startup, and identify sentiment in user comments [8] . Artificial intelligence techniques, such as natural language processing (NLP) technology, enable sentiment analysis in social media, including Twitter, by identifying and classifying data based on user sentiment towards a specific topic, product, or brand [9] . Sentiment analysis can provide valuable insights for companies to improve their marketing and customer service strategies [10] . Companies can use this information to inform their marketing strategies, enhance customer service, and monitor public perception over time [11] . Sentiment analysis has become an important tool in understanding people's feelings on a variety of topics, including public housing savings programs.  \nThe policy on People's Housing Savings (Tapera) has raised pros and co","cbCaipC8hE2zxUnb","https://ap.wps.com/l/cbCaipC8hE2zxUnb","pdf",863585,1,10,"English","en",105,"# I. INTRODUCTION\n## Social media and sentiment analysis\n## Tapera policy background and challenges","[{\"question\":\"What problem does the study address in public sentiment analysis of the Tapera policy?\",\"answer\":\"It focuses on selecting the most effective machine learning algorithm to classify public opinion toward the Tapera policy with high accuracy using Twitter text data.\"},{\"question\":\"Which machine learning algorithms are compared in the research?\",\"answer\":\"The study compares Naïve Bayes, Support Vector Machine (SVM), and Random Forest.\"},{\"question\":\"What accuracy results were obtained for each algorithm?\",\"answer\":\"Naïve Bayes achieved 69.17%, SVM achieved 68.42%, and Random Forest achieved 66.17%.\"}]","Comparison Of Machine Learning Algorithms In Public Sentiment Analysis Of TAPERA Policy | PDF",1785808805,25,{"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},"comparison-of-machine-learning-algorithms-in-public-sentiment-analysis-of-tapera-policy","",{"@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/comparison-of-machine-learning-algorithms-in-public-sentiment-analysis-of-tapera-policy/122098/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address in public sentiment analysis of the Tapera policy?","Question",{"text":75,"@type":76},"It focuses on selecting the most effective machine learning algorithm to classify public opinion toward the Tapera policy with high accuracy using Twitter text data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are compared in the research?",{"text":80,"@type":76},"The study compares Naïve Bayes, Support Vector Machine (SVM), and Random Forest.",{"name":82,"@type":73,"acceptedAnswer":83},"What accuracy results were obtained for each algorithm?",{"text":84,"@type":76},"Naïve Bayes achieved 69.17%, SVM achieved 68.42%, and Random Forest achieved 66.17%.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]