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Penelitian ini mengekstrak dan mengolah tanggapan pengguna Twitter agar menjadi informasi berbasis analisis sentimen, dengan klasifikasi menggunakan Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), dan K-Nearest Neighbor (KNN). Hasil menunjukkan dominasi sentimen negatif (50,1%), diikuti netral (30,5%) dan positif (19,3%). Kinerja tertinggi diperoleh SVM dengan akurasi 90%, disusul MNB 71% dan KNN 48%.",{"@graph":63,"@context":124},[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/sentiment-analysis-of-tweets-about-the-russia-vs-ukraine-event-using-machine-learning-academic-journal/126975/",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/sentiment-analysis-of-tweets-about-the-russia-vs-ukraine-event-using-machine-learning-academic-journal/126975.png","ImageObject",300,407,{"name":89,"@type":90},"Liam","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",[106,112,116,120],{"name":107,"@type":108,"acceptedAnswer":109},"Tujuan penelitian ini apa?","Question",{"text":110,"@type":111},"Mengolah serta mengekstrak tanggapan masyarakat di Twitter menjadi informasi melalui analisis sentimen.","Answer",{"name":113,"@type":108,"acceptedAnswer":114},"Metode klasifikasi apa yang digunakan untuk analisis sentimen?",{"text":115,"@type":111},"Penelitian menggunakan Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), dan K-Nearest Neighbor (KNN).",{"name":117,"@type":108,"acceptedAnswer":118},"Bagaimana hasil distribusi sentimen tweet pada 24 Februari 2022?",{"text":119,"@type":111},"Tweet didominasi sentimen negatif (50,1%), kemudian netral (30,5%) dan positif (19,3%).",{"name":121,"@type":108,"acceptedAnswer":122},"Algoritma mana yang menghasilkan performa tertinggi dan berapa akurasinya?",{"text":123,"@type":111},"Model dengan algoritma SVM memberikan performa tertinggi dengan akurasi 90%, dibanding MNB (71%) dan KNN (48%).","https://schema.org",{"og:url":79,"og:type":126,"og:title":59,"og:site_name":92,"og:description":61},"article",{"robots":128,"canonical":79},"index,follow",{"doc_id":130,"site_id":56},126975,1785935991,{"code":4,"msg":5,"data":133},{"doc_id":130,"user_id":134,"nickname":89,"user_avatar":135,"doc_module":4,"category_id":30,"category_name":31,"doc_title":59,"doc_description":61,"doc_content":136,"file_id":137,"file_url":138,"file_type":139,"file_size":140,"view_count":102,"is_deleted":4,"is_public":71,"is_downloadable":71,"audit_status":71,"page_count":141,"language":142,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":143,"faqs":144,"seo_title":145,"seo_description":61,"update_tm":131,"read_time":146},687207024478,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Syntax Literate: Jurnal Ilmiah Indonesia p–ISSN: 2541-0849  \ne-ISSN: 2548-1398  \nVol. 9, No. 2, Februari 2024   \nANALISIS SENTIMEN TERHADAP TWEET MENGENAI PERISTIWA RUSSIA MELAWAN UKRAINA BERBASIS MACHINE LEARNING  \nMuhammad Husni Mubarok1, Jati Sasongko Wibowo2  \nUniversitas Stikubank Semarang, Jawa Tengah, Indonesia  \n[Email: muhammadhusnimubarok@unisbank.ac.id](Email: muhammadhusnimubarok@unisbank.ac.id1)[1](Email: muhammadhusnimubarok@unisbank.ac.id1), [jatisw@edu.unisbank.ac.id](jatisw@edu.unisbank.ac.id2)[2](jatisw@edu.unisbank.ac.id2)  \nAbstrak  \nPerang yang terjadi di Eropa antara Rusia - Ukraina berdampak langsung maupun tidaklangsung diseluruh belahan dunia, termasuk Indonesia. Masyarakat banyak yang memberikan pendapat atas peristiwa tersebut, baik itu berupa pujian atau keluh kesah yang dipublikasikan pada media sosial, salah satunya adalah Twitter. Penelitian ini bertujuan untuk mengolah atau mengekstrak tanggapan masyarakat di media Twitter agar menjadisebuah informasi dengan menggunakan analisis sentiment. Analisis sentiment denganalgoritma klasifikasi Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), dan K-Nearest Neigbhor (KKN) . Berdasarkan analisis sentimen memberikan gambaran bahwa tanggapan masyarakat Indonesia pengguna twitter pada tanggal 24 Februari 2022 tentang peristiwa perang Rusia - Ukraina didominasi dengan tweet bersentimen negatif (50,1%), kemudian netral (30,5%), dan positif (19,3%) . Hasil performa tertinggi model machine learning didapat pada model beralgoritma SVM dengan akurasi 90%, diikuti oleh MNB (71%), dan KKN (48%) . Masyarkat Indonesia banyak yang mengaitkan peristiwa perang Rusia vs Ukraina dengan peristiwa perang dunia.  \nKata kunci : Analisis Sentimen; Twitter; Support Vector Machine; Multinomial Naïve Bayes; K-Nearest Neighbor  \nAbstract  \nThe war that took place in Europe between Russia and Ukraine had a direct or indirect impact on all parts of the world, including Indonesia. Many people gave their opinion on the incident, whether it was in the form of praise or complaints published on social media, one of which was Twitter. This study aims to process or extract public responses on Twitter media to become information using sentiment analysis. Sentiment analysis using the classification algorithm Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), and K-Nearest Neigbhor (KKN). Based on sentiment analysis, it illustrates that the response of the Indonesian public on Twitter users on February 24, 2022 about the events of the Russia-Ukraine war was dominated by tweets with negative sentiments (50.1%), then neutral (30.5%), and positive (19.3%). The highest performance results from the machine learning model were obtained on the SVM algorithm model with an accuracy of 90%, followed by MNB (71%), and KKN (48%). Many Indonesian people associate the events of the Russia vs Ukraine war with the events of the world war.  \nKeywords: Sentiment Analysis; Twitter; Support Vector Machines; Naïve Bayes Multinomial; K-Nearest Neighbor  \n\n| How to cite: | Mubarok, M. H., & Wibowo, J. S. (2024) . Analisis Sentimen Terhadap Tweet Mengenai Peristiwa Russia Melawan Ukraina Berbasis Machine Learning. Syntax Literate. (9)2.\u003Cbr>[http://dx.doi.org/10.36418/syntax-literate.v9i2](http://dx.doi.org/10.36418/syntax-literate.v9i2) |\n| --- | --- |\n| E-ISSN: | 2548-1398 |\n| Published by: | Ridwan Institute |\n\nAnalisis Sentimen Terhadap Tweet Mengenai Peristiwa Russia Melawan Ukraina Berbasis Machine Learning  \nPendahuluan  \nPada saat ini, perang yang terjadi di Eropa antara Rusia melawan Ukrainaberdampak di berbagai negara dibelahan penjuru dunia, tak terkecuali negara Indonesia (Hutabarat, 2022) . Sektor ekonomi merupakan sektor yang sangat berdampak akibat perang yang terjadi antara Rusia melawan Ukraina (Bakrie et al., 2022 ; Rubel & Hossain, 2022) . Dampak yang terjadi ialah mulai dari nilai tukar rupiah yang turunterhadap Dollar AS, kehilangan pendapatan dari ekspor terhadap kedua negara","cbCaigeO5g3m5LpQ","https://ap.wps.com/l/cbCaigeO5g3m5LpQ","pdf",894060,15,"Indonesian","# Pendahuluan\n## Latar belakang dampak perang dan opini di Twitter\n## Konsep analisis sentimen dan kebutuhan klasifikasi\n## Riset terdahulu dan perbandingan metode","[{\"question\":\"Tujuan penelitian ini apa?\",\"answer\":\"Mengolah serta mengekstrak tanggapan masyarakat di Twitter menjadi informasi melalui analisis sentimen.\"},{\"question\":\"Metode klasifikasi apa yang digunakan untuk analisis sentimen?\",\"answer\":\"Penelitian menggunakan Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), dan K-Nearest Neighbor (KNN).\"},{\"question\":\"Bagaimana hasil distribusi sentimen tweet pada 24 Februari 2022?\",\"answer\":\"Tweet didominasi sentimen negatif (50,1%), kemudian netral (30,5%) dan positif (19,3%).\"},{\"question\":\"Algoritma mana yang menghasilkan performa tertinggi dan berapa akurasinya?\",\"answer\":\"Model dengan algoritma SVM memberikan performa tertinggi dengan akurasi 90%, dibanding MNB (71%) dan KNN (48%).\"}]","Analisis Sentimen Terhadap Tweet Mengenai Peristiwa Rusia Melawan Ukraina Berbasis Machine Learning - Jurnal ilmiah | PDF",23]