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Penelitian ini menganalisis sentimen opini publik terhadap implementasi TAPERA dengan membandingkan berbagai metode Machine Learning dan Deep Learning. Data diperoleh melalui web scraping menggunakan platform Apify pada Mei–Juli 2024, menghasilkan 5.580 tweet. Sebelum klasifikasi, dilakukan preprocessing NLP untuk menangani variasi bahasa informal, slang, serta penghapusan elemen non-tekstual. Hasil menunjukkan mayoritas opini bersifat negatif. Decision Tree dan SVM mencapai akurasi tertinggi 0,99, sedangkan LSTM 0,5624, memberikan dasar untuk pengambilan keputusan strategis terkait kebijakan perumahan nasional.",{"@graph":63,"@context":120},[64,81,103],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,72,75,78],{"item":68,"name":69,"@type":70,"position":71},"https://docshare.wps.com","Home","ListItem",1,{"item":73,"name":9,"@type":70,"position":74},"https://docshare.wps.com/id/document/",2,{"item":76,"name":31,"@type":70,"position":77},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":79,"name":59,"@type":70,"position":80},"https://docshare.wps.com/id/document/comparison-of-machine-learning-methods-for-sentiment-analysis-of-public-opinion-on-tapera-peoples-housing-savings/127457/",4,{"url":79,"name":59,"@type":82,"image":83,"author":88,"headline":59,"publisher":91,"fileFormat":94,"inLanguage":57,"description":61,"dateModified":95,"datePublished":96,"encodingFormat":94,"isAccessibleForFree":97,"interactionStatistic":98},"DigitalDocument",{"url":84,"@type":85,"width":86,"height":87},"https://docshare.wps.com/thumbnails/comparison-of-machine-learning-methods-for-sentiment-analysis-of-public-opinion-on-tapera-peoples-housing-savings/127457.png","ImageObject",300,407,{"name":89,"@type":90},"Ophelia","Person",{"url":68,"name":92,"@type":93},"DocShare","Organization","application/pdf","2026-09-19","2026-08-05",true,{"@type":99,"interactionType":100,"userInteractionCount":102},"InteractionCounter",{"@type":101},"ViewAction",6,{"@type":104,"mainEntity":105},"FAQPage",[106,112,116],{"name":107,"@type":108,"acceptedAnswer":109},"Penelitian ini bertujuan untuk apa?","Question",{"text":110,"@type":111},"Penelitian bertujuan menganalisis sentimen opini publik terhadap implementasi TAPERA dengan membandingkan metode Machine Learning dan Deep Learning.","Answer",{"name":113,"@type":108,"acceptedAnswer":114},"Bagaimana data tweet dikumpulkan dan dipersiapkan sebelum klasifikasi?",{"text":115,"@type":111},"Data dikumpulkan melalui web scraping menggunakan platform Apify pada Mei–Juli 2024. Sebelum klasifikasi, dilakukan preprocessing NLP untuk menangani variasi bahasa informal, slang, serta menghapus elemen non-tekstual.",{"name":117,"@type":108,"acceptedAnswer":118},"Model mana yang memperoleh akurasi tertinggi dan berapa nilainya?",{"text":119,"@type":111},"Decision Tree dan Support Vector Machine (SVM) memperoleh akurasi tertinggi masing-masing 0,99 berdasarkan Confusion Matrix. Model LSTM memperoleh akurasi 0,5624.","https://schema.org",{"og:url":79,"og:type":122,"og:title":59,"og:site_name":92,"og:description":61},"article",{"robots":124,"canonical":79},"index,follow",{"doc_id":126,"site_id":56},127457,1785938975,{"code":4,"msg":5,"data":129},{"doc_id":126,"user_id":130,"nickname":89,"user_avatar":131,"doc_module":4,"category_id":30,"category_name":31,"doc_title":59,"doc_description":61,"doc_content":132,"file_id":133,"file_url":134,"file_type":135,"file_size":136,"view_count":102,"is_deleted":4,"is_public":71,"is_downloadable":71,"audit_status":71,"page_count":137,"language":138,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":139,"faqs":140,"seo_title":141,"seo_description":61,"update_tm":127,"read_time":142},962084925290,"https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","PERBANDINGAN METODE MACHINE  \nLEARNING UNTUK ANALISIS SENTIMEN OPINI PUBLIK TENTANG TAPERA  \n(TABUNGAN PERUMAHAN RAKYAT)  \n1Gery Octavansyah, 2Bahrul Ulum  \n1,2Teknik Informatika, Fakultas Ilmu Komputer, Universitas Esa Unggul,  \nDKI Jakarta  \n[E-mail:](E-mail:1geryoctavansyah17@student.esaunggul.ac.id)[1](E-mail:1geryoctavansyah17@student.esaunggul.ac.id)[geryoctavansyah17@student.esaunggul.ac.id](E-mail:1geryoctavansyah17@student.esaunggul.ac.id)  \n[2](2m.bahrul_ulum@esaunggul.ac.id)[m.bahrul_ulum@esaunggul.ac.id](2m.bahrul_ulum@esaunggul.ac.id)  \nABSTRAK  \nKebijakan Tabungan Perumahan Rakyat (TAPERA) telah memicu perdebatan luas di masyarakat Indonesia, terutama di platform media sosial X (sebelumnya Twitter) . Penelitian ini bertujuan untuk menganalisis sentimen opini publik terhadap implementasi TAPERA dengan membandingkan berbagaimetode Machine Learning dan Deep Learning. Data dikumpulkan menggunakan teknik web scraping melalui platform Apify dalam rentang waktu Mei hingga Juli 2024, menghasilkan total 5.580 tweet. Sebelum tahap klasifikasi, data melalui proses preprocessing menggunakan teknik Natural Language Processing (NLP) untuk menangani variasi bahasa informal, slang, dan penghapusan elemen non-tekstual. Hasil analisis menunjukkan bahwa mayoritas opini publik terhadap TAPERA bersifat negatif. Berdasarkan pengujian performa, model Decision Tree dan Support Vector Machine (SVM) memberikantingkat akurasi tertinggi masing-masing sebesar 0,99 pada Confusion Matrix. Sementara itu, model Deep Learning LSTM memberikan hasil akurasi sebesar 0,5624. Penelitian ini memberikan kontribusi praktisbagi pemangku kepentingan dalam memahami dinamika opini publik sebagai dasar pengambilan keputusan strategis terkait kebijakan perumahan nasional.  \nKata kunci :Analisis Sentimen, TAPERA, Machine Learning, Deep Learning, MediaSosial X.  \nABSTRACT  \nThe People's Housing Savings Policy (TAPERA) has sparked widespread debate in Indonesian society, especially on the social media platform X (formerly Twitter). This study aims to analyze the sentiment of public opinion on the implementation of TAPERA by comparing various Machine Learning and Deep Learning methods. The data was collected using web scraping techniques through the Apify platform in the period from May to July 2024, resulting in a total of 5,580 tweets. Before the classification stage, the data goes through a preprocessing process using Natural Language Processing (NLP) techniques to handle informal language variations, slang, and the removal of non-textual elements. The results of the analysis show that the majority of public opinion towards TAPERA is negative. Based on performance testing, the Decision Tree and Support Vector Machine (SVM) models provided the highest accuracy level of 0.99 each on the Confusion Matrix. Meanwhile, the LSTM Deep Learning model gave an accuracy of 0.5624. This research provides practical contributions for stakeholders in understanding the dynamics of public opinion as the basis for strategic decision-making related to national housing policy.  \nKeyword : Analisis Sentimen, TAPERA, Machine Learning, Deep Learning, MediaSosial X  \n1. PENDAHULUAN  \nKebijakan Tabungan Perumahan Rakyat (TAPERA) yang diluncurkan oleh Pemerintah Indonesia bertujuan untuk menghimpun dan menyediakan dana murah jangka panjang yang berkelanjutan untuk pembiayaan perumahan bagi masyarakat. Meskipun memiliki tujuan mulia untuk mengatasi backlog perumahan,  \nimplementasi kebijakan ini memicu pro dankontra yang masif di tengah masyarakat (Palanisamy et al., 2013) . Hal ini terlihat dari tingginya volume diskusi di platform mediasosial, khususnya X (sebelumnya Twitter), yang menjadi saluran utama masyarakatdalam menyampaikan aspirasi, keluhan, maupun dukungan secara real-time.  \nOpini publik di media sosial merupakan data tekstual yang sangat berharga namun memiliki volume yang sangat besar dan tidak terstruktur. Untuk memahami  \nkecenderungan suara masyarakat secara efektif, dip","cbCaiciT9eRpgd5u","https://ap.wps.com/l/cbCaiciT9eRpgd5u","pdf",1470554,10,"Indonesian","# Pendahuluan\n## Latar belakang TAPERA dan perdebatan publik\n## Analisis sentimen di media sosial\n## Tantangan bahasa Indonesia informal dan slang\n## Penelitian terdahulu dan tujuan studi\n# Landasan Teori\n## TAPERA (Tabungan Perumahan Rakyat)","[{\"question\":\"Penelitian ini bertujuan untuk apa?\",\"answer\":\"Penelitian bertujuan menganalisis sentimen opini publik terhadap implementasi TAPERA dengan membandingkan metode Machine Learning dan Deep Learning.\"},{\"question\":\"Bagaimana data tweet dikumpulkan dan dipersiapkan sebelum klasifikasi?\",\"answer\":\"Data dikumpulkan melalui web scraping menggunakan platform Apify pada Mei–Juli 2024. Sebelum klasifikasi, dilakukan preprocessing NLP untuk menangani variasi bahasa informal, slang, serta menghapus elemen non-tekstual.\"},{\"question\":\"Model mana yang memperoleh akurasi tertinggi dan berapa nilainya?\",\"answer\":\"Decision Tree dan Support Vector Machine (SVM) memperoleh akurasi tertinggi masing-masing 0,99 berdasarkan Confusion Matrix. Model LSTM memperoleh akurasi 0,5624.\"}]","PERBANDINGAN METODE MACHINE LEARNING UNTUK ANALISIS SENTIMEN OPINI PUBLIK TENTANG TAPERA (TABUNGAN PERUMAHAN RAKYAT) | PDF",15]