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Kendaraan listrik diposisikan sebagai alternatif yang lebih ramah lingkungan karena minim emisi polusi. Penelitian ini menganalisis sentimen pengguna media sosial X terhadap kendaraan listrik menggunakan teknik machine learning untuk mengidentifikasi respons yang mendukung maupun menolak. Tahapan meliputi pengumpulan, seleksi, pra-pemrosesan, lalu klasifikasi dengan Naïve Bayes Classifier, Support Vector Machine, dan K-Nearest Neighbor. Pada dataset seimbang dengan ROS, SVM terbaik dengan akurasi 68,7%, presisi 77,9%, recall 68,4%, sementara NBC dan KNN menunjukkan kinerja lebih rendah.",{"@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/machine-learning-approach-sentiment-analysis-of-public-toward-electric-vehicles-on-social-media-x-naive-bayes-svm-and-knn-classification-study/126942/",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/machine-learning-approach-sentiment-analysis-of-public-toward-electric-vehicles-on-social-media-x-naive-bayes-svm-and-knn-classification-study/126942.png","ImageObject",300,407,{"name":89,"@type":90},"Noah","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",8,{"@type":104,"mainEntity":105},"FAQPage",[106,112,116],{"name":107,"@type":108,"acceptedAnswer":109},"Penelitian ini bertujuan untuk menganalisis apa?","Question",{"text":110,"@type":111},"Penelitian menganalisis sentimen pengguna media sosial X terhadap kendaraan listrik, mencakup respons yang mendukung maupun yang menolak.","Answer",{"name":113,"@type":108,"acceptedAnswer":114},"Algoritma machine learning apa yang digunakan untuk klasifikasi sentimen?",{"text":115,"@type":111},"Penelitian menggunakan Naïve Bayes Classifier (NBC), Support Vector Machine (SVM), dan K-Nearest Neighbor (KNN).",{"name":117,"@type":108,"acceptedAnswer":118},"Bagaimana performa model terbaik pada dataset seimbang menggunakan ROS?",{"text":119,"@type":111},"Model SVM memberikan hasil terbaik dengan akurasi 68,7%, presisi 77,9%, dan recall 68,4%.","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},126942,1785935795,{"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},137451207643,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","p-ISSN: 2460-092X, e-ISSN: 2623-1662 Vol. 9, No. 2, Desember 2023  \nHal. 65-76  \nPendekatan Machine Learning: Analisis Sentimen Masyarakat Terhadap Kendaraan Listrik Pada Sosial Media X  \nGathot Hanyokro Kusuma*, Inggih Permana, Febi Nur Salisah, M. Afdal, Muhammad Jazman, Arif Marsal  \n[12050313348@students.uin-suska.ac.id](12050313348@students.uin-suska.ac.id)*  \n*Penulis korespondensi  \nUniversitas Islam Negeri Sultan Syarif Kasim Riau-Indonesia  \nDiterima: 27 Nov 2023 | Direvisi: 04 – 09 Des 2023  \nDisetujui: 26 Des 2023 | Dipublikasi: 30 Des 2023  \nProgram Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang, Indonesia  \n\n| ABSTRACT |\n| --- |\n| Environmental issues and the depletion of fossil fuels continue to escalate as the number of fossil fuel-based vehicle users increases in Indonesia. Electric vehicles emerge as one of the potential alternative solutions to address current environmental challenges, given their eco-friendly nature and lack of pollution emissions. Sentiment analysis is conducted to understand public responses, both supportive and opposing, towards electric vehicles. This research aims to analyze the sentiment of X-social media users regarding electric vehicles using machine learning techniques. The research stages include data collection, data selection, preprocessing, and classification using Naïve Bayes Classifier (NBC), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms. The test results show that on a balanced dataset using ROS, SVM performs the best with accuracy = 68.7%, precision = 77.9%, and recall = 68.4%. Meanwhile, NBC yields an accuracy of 60.3%, precision of 61.3%, and recall of 60.3%, while KNN has an accuracy of 53.9%, precision of 54%, and recall of 53.9%.\u003Cbr>Keywords: Sentiment Analysis, K-Nearest Neighbor, Naïve Bayes Classifier, Support Vector Machine |\n| ABSTRAK |\n\nMasalah lingkungan dan penipisan bahan bakar minyak bumi terus meningkat karenajumlah pengguna kendaraan dengan bahan bakar minyak bumi terus meningkat di Indonesia. Kendaraan listrik menjadi salah satu solusi alternatif potensial untuk mengatasi tantangan lingkungan saat ini, mengingatsifatnya yang ramah lingkungan dan tidak menyebabkan polusi. Analisis sentimen dilakukan untuk memahami tanggapan masyarakat, baik yang mendukung maupun yang tidak mendukung terhadap kendaraan listrik. Penelitian ini bertujuan untuk menganalisis sentimen pengguna media sosial X mengenai kendaraan listrik dengan menggunakan teknik machine learning. Tahapan penelitian mencakup pengumpulan data, seleksi data, pra-pemrosesan, dan klasifikasi menggunakan algoritma Naïve Bayes Classifier (NBC), Support Vector Machine (SVM), dan K-Nearest Neighbor (KNN). Hasil pengujian menunjukkan bahwapada dataset yang seimbang menggunakan ROS, SVM memberikan kinerja terbaik dengan akurasi = 68,7%, presisi = 77,9%, dan recall = 68,4%. Sementara itu, NBC menghasilkan akurasi 60,3%, presisi 61,3%, dan recall 60,3%, sedangkan KNN memiliki akurasi 53,9%, presisi 54%, dan recall 53,9%.  \nKata Kunci: Analisis Sentimen, K-Nearest Neighbor, Naïve Bayes Classifier, Support Vector Machine  \nPENDAHULUAN  \nJumlah kendaraan bermotor di Indonesia terus meningkat dari tahun ke tahun, meliputi sepeda motor, kendaraan mobil seperti mobil penumpang, mobil barang, dan mobil bis (Sidik & Ansawarman, 2022) . Peningkatan jumlah kendaraan bermotor tersebut menunjukkan bahwa penggunaan Bahan Bakar Minyak (BBM) menjadi terus bertambah banyak sesuai dengan peningkatan tersebut. Menurut data Badan Pusat Statistik, jumlah kendaraan bermotor terus bertambah dari 126,5 juta unit pada tahun 2018 menjadi 148,3 juta unit pada tahun 2022 (Badan Pusat Statistik, 2022) . Dengan hampir seluruh kendaraan menggunakan BBM, peningkatan jumlah pengguna kendaraan telah menyebabkan stok BBM yang semakin menipis dan kenaikan harga BBM (Erfina & Lestari, 2023). Selain itu, kendaraan yang menggunakan BBM jugamenjadi penyumbang emisi dan penc","cbCaigKkUR3vwER7","https://ap.wps.com/l/cbCaigKkUR3vwER7","pdf",552842,12,"Indonesian","# Pendahuluan\n## Latar belakang peningkatan kendaraan berbasis BBM\n## Kebijakan dan respons masyarakat di media sosial X\n# Dasar Teori\n## Analisis sentimen dan pengolahan data tekstual\n# Metode Penelitian\n## Pengumpulan data, seleksi, dan pra-pemrosesan\n## Klasifikasi dengan Naïve Bayes, SVM, dan KNN\n# Hasil dan Pembahasan\n## Evaluasi performa pada dataset seimbang dengan ROS\n# Kesimpulan","[{\"question\":\"Penelitian ini bertujuan untuk menganalisis apa?\",\"answer\":\"Penelitian menganalisis sentimen pengguna media sosial X terhadap kendaraan listrik, mencakup respons yang mendukung maupun yang menolak.\"},{\"question\":\"Algoritma machine learning apa yang digunakan untuk klasifikasi sentimen?\",\"answer\":\"Penelitian menggunakan Naïve Bayes Classifier (NBC), Support Vector Machine (SVM), dan K-Nearest Neighbor (KNN).\"},{\"question\":\"Bagaimana performa model terbaik pada dataset seimbang menggunakan ROS?\",\"answer\":\"Model SVM memberikan hasil terbaik dengan akurasi 68,7%, presisi 77,9%, dan recall 68,4%.\"}]","Pendekatan Machine Learning - Analisis Sentimen Masyarakat Terhadap Kendaraan Listrik Pada Sosial Media X - Studi Klasifikasi Naive Bayes, SVM, dan KNN | PDF",18]