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Penelitian ini membandingkan kinerja beberapa model machine learning dan deep learning dalam menganalisis sentimen terkait fenomena fufufafa pada Twitter. Model yang diuji meliputi Naive Bayes, Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, serta Long Short-Term Memory (LSTM). Hasil menunjukkan Decision Tree unggul dengan akurasi 96%, diikuti LSTM 95%.",{"@graph":63,"@context":119},[64,81,102],{"@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-performance-of-machine-learning-and-deep-learning-for-sentiment-analysis-of-fufufafa-read-online-free/291066/",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-performance-of-machine-learning-and-deep-learning-for-sentiment-analysis-of-fufufafa-read-online-free/291066.png","ImageObject",300,407,{"name":89,"@type":90},"Theodora","Person",{"url":68,"name":92,"@type":93},"DocShare","Organization","application/pdf","2026-09-27","2026-09-17",true,{"@type":99,"interactionType":100,"userInteractionCount":80},"InteractionCounter",{"@type":101},"ViewAction",{"@type":103,"mainEntity":104},"FAQPage",[105,111,115],{"name":106,"@type":107,"acceptedAnswer":108},"Apa tujuan utama penelitian ini?","Question",{"text":109,"@type":110},"Menilai efektivitas setiap model dalam mengklasifikasikan sentimen secara akurat pada fenomena fufufafa di Twitter.","Answer",{"name":112,"@type":107,"acceptedAnswer":113},"Model apa saja yang dibandingkan untuk analisis sentimen?",{"text":114,"@type":110},"Naive Bayes, Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, serta Long Short-Term Memory (LSTM).",{"name":116,"@type":107,"acceptedAnswer":117},"Model mana yang memperoleh akurasi tertinggi dan berapa nilainya?",{"text":118,"@type":110},"Decision Tree mencapai akurasi tertinggi sebesar 96%, diikuti LSTM dengan akurasi 95%.","https://schema.org",{"og:url":79,"og:type":121,"og:title":59,"og:site_name":92,"og:description":61},"article",{"robots":123,"canonical":79},"index,follow",{"doc_id":125,"site_id":56},291066,1789643355,{"code":4,"msg":5,"data":128},{"doc_id":125,"user_id":129,"nickname":89,"user_avatar":130,"doc_module":4,"category_id":30,"category_name":31,"doc_title":59,"doc_description":61,"doc_content":131,"file_id":132,"file_url":133,"file_type":134,"file_size":135,"view_count":80,"is_deleted":4,"is_public":71,"is_downloadable":71,"audit_status":71,"page_count":136,"language":137,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":138,"faqs":139,"seo_title":140,"seo_description":61,"update_tm":126,"read_time":141},687197207919,"https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552","Perbandingan Kinerja Machine Learning dan Deep Learning untuk Analisis Sentimen Fufufafa  \nDarusman1,*, Windu Gata1  \n1 Program Studi Magister Ilmu Komputer; Universitas Nusa Mandiri Jakarta; Jl. Raya Jatiwaringin Cipinang Melayu, Kec. Makasar, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, No.Telpon (021)28534471; e-mail: [14230029@nusamandiri.ac.id](14230029@nusamandiri.ac.id) ;  \n[windu@nusamandiri.ac.id](windu@nusamandiri.ac.id) ;  \n* Korespondensi: e-mail: [14230029@nusamandiri.ac.id](14230029@nusamandiri.ac.id)[ ](14230029@nusamandiri.ac.id)Diterima: 14 Januari 2025; Review: 03 Mei 2025; Disetujui: 16 Juni 2025  \nCara sitasi: Darusman D, Gata W. 2025. Perbandingan Kinerja Machine Learning dan Deep Learning untuk Analisis Sentimen Fufufafa. Information System for Educators and Professionals. Vol 10(1): 1-12.  \nAbstrak: Analisis sentimen pada data media sosial, khususnya Twitter, menghadapi tantangandalam mengklasifikasikan opini yang dinamis dan kompleks ke dalam kategori positif, negatif, dan netral. Penelitian ini membandingkan kinerja beberapa model machine learning dan deep learning dalam menganalisis sentimen terhadap fenomena fufufafa di Twitter. Model yang diujimeliputi Naive Bayes, Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, dan Long Short-Term Memory (LSTM) . Tujuan utama penelitian ini adalah mengevaluasiefektivitas masing-masing model dalam mengklasifikasikan sentimen secara akurat. Hasileksperimen menunjukkan bahwa Decision Tree mencapai akurasi tertinggi sebesar 96%, diikuti oleh LSTM dengan 95% . Model Random Forest, SVM, dan Logistic Regression mencatatakurasi sebesar 94%, sedangkan Naive Bayes memiliki akurasi terendah yaitu 81%, terutamakarena keterbatasannya dalam menangani sentimen yang kompleks. Model LSTM unggul dalam menangkap konteks temporal dan hubungan antar kata sehingga memberikan prediksi lebih akurat meski membutuhkan sumber daya komputasi lebih besar. Temuan ini menegaskan bahwa deep learning, khususnya LSTM, lebih efektif dalam menganalisis sentimen pada data sosial media yang dinamis dibandingkan metode machine learning konvensional. Penelitian ini memberikan dasar untuk pengembangan metode analisis sentimen yang lebih efisien danakurat di masa depan.  \nKata kunci: Analisis Sentimen , Media Sosial Twitter , Machine Learning, Deep Learning  \nAbstract: Sentiment analysis on social media data, particularly Twitter, faces challenges in classifying dynamic and complex opinions into positive, negative, and neutral categories. This study compares the performance of several machine learning and deep learning models in analyzing sentiments related to the fufufafa phenomenon on Twitter. The models tested include Naive Bayes, Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and Long Short-Term Memory (LSTM). The primary objective is to evaluate the effectiveness of each model in accurately classifying sentiments. Experimental results show that the Decision Tree achieved the highest accuracy of 96%, followed by LSTM with 95%. Random Forest, SVM, and Logistic Regression recorded accuracies of 94%, while Naive Bayes had the lowest accuracy at 81%, mainly due to its limitations in handling complex sentiments. The LSTM model excels in capturing temporal context and word relationships, providing more accurate predictions despite requiring greater computational resources. These findings confirm that deep learning, particularly LSTM, is more effective in analyzing sentiments on dynamic social media data compared to conventional machine learning methods. This study provides a foundation for developing more efficient and accurate sentiment analysis methodologies in the future.  \nKeywords: Sentiment Analysis, Twitter Social Media, Machine Learning, Deep Learning  \n1. Pendahuluan  \nDalam era digital saat ini, media sosial telah menjadi platform penting bagi individu untuk berbagi informasi, opini, dan perasaan terhadap berbagai isu yang sedang berkemba","cbCaijjcSviLBDhE","https://ap.wps.com/l/cbCaijjcSviLBDhE","pdf",704374,12,"Indonesian","# Pendahuluan\n## Latar belakang media sosial dan Twitter\n## Relevansi fenomena fufufafa\n## Peran model transformer dan IndoBERT","[{\"question\":\"Apa tujuan utama penelitian ini?\",\"answer\":\"Menilai efektivitas setiap model dalam mengklasifikasikan sentimen secara akurat pada fenomena fufufafa di Twitter.\"},{\"question\":\"Model apa saja yang dibandingkan untuk analisis sentimen?\",\"answer\":\"Naive Bayes, Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, serta Long Short-Term Memory (LSTM).\"},{\"question\":\"Model mana yang memperoleh akurasi tertinggi dan berapa nilainya?\",\"answer\":\"Decision Tree mencapai akurasi tertinggi sebesar 96%, diikuti LSTM dengan akurasi 95%.\"}]","Perbandingan Kinerja Machine Learning dan Deep Learning untuk Analisis Sentimen Fufufafa - read online free | PDF",18]