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Sentimen diklasifikasikan ke dalam positif, negatif, dan netral, sekaligus mengidentifikasi gaya bahasa kompleks seperti ironi, sindiran, sarkasme, satire, dan metafora. Naive Bayes digunakan untuk klasifikasi sentimen dasar, sementara GPT-40 menafsirkan makna mendalam pada gaya bahasa yang tidak eksplisit. Business Intelligence mendukung visualisasi data dan keputusan yang lebih cepat. Temuan menegaskan bahwa perhatian terhadap konteks budaya dan linguistik meningkatkan akurasi pemahaman sentimen serta memperkuat strategi branding universitas di era digital.",{"@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/optimizing-sentiment-management-on-university-social-media-through-machine-learning-and-ai-case-study-on-instagram-comments-journal/128134/",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/optimizing-sentiment-management-on-university-social-media-through-machine-learning-and-ai-case-study-on-instagram-comments-journal/128134.png","ImageObject",300,407,{"name":89,"@type":90},"Rowan","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",11,{"@type":104,"mainEntity":105},"FAQPage",[106,112,116],{"name":107,"@type":108,"acceptedAnswer":109},"Penelitian ini menggunakan pendekatan apa untuk mengelola sentimen di media sosial universitas?","Question",{"text":110,"@type":111},"Penelitian menggunakan manajemen sentimen berbasis algoritma Naive Bayes dan teknologi AI GPT-40 untuk menganalisis komentar di Instagram.","Answer",{"name":113,"@type":108,"acceptedAnswer":114},"Bagaimana sentimen diklasifikasikan dalam penelitian ini?",{"text":115,"@type":111},"Sentimen diklasifikasikan menjadi positif, negatif, dan netral, dengan penekanan pada interpretasi makna dari gaya bahasa yang tidak eksplisit.",{"name":117,"@type":108,"acceptedAnswer":118},"Mengapa konteks budaya dan linguistik penting dalam analisis sentimen pada penelitian ini?",{"text":119,"@type":111},"Pendekatan yang memperhatikan konteks budaya dan linguistik menghasilkan pemahaman sentimen yang lebih akurat dan memberi wawasan untuk strategi branding universitas.","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},128134,1785945010,{"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},3985741905716,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","Volume 11 Nomor 1, 2025, 31-38 ISSN: 2460-1799 P-ISSN: 1432-602743  \nOptimalisasi Manajemen Sentimen di Media Sosial Universitas melalui Machine Learning dan AI: Studi Kasus pada Komentar Instagram  \nSri Pernia Warakmulty, dan Yeffry Handoko Putra*  \nMagister Sistem Informasi, Universitas Komputer Indonesia, Jl. Dipati Ukur 112-116 Bandung [e-mail: niah@email.unikom.ac.id](e-mail: niah@email.unikom.ac.id), [yeffryhandoko@email.unikom.ac.id](yeffryhandoko@email.unikom.ac.id)*  \nDiterima: 18/03/2025; Review: 29/04/2025; Disetujui: 10/05/2025  \nABSTRAK– Penelitian ini mengeksplorasi efektivitas penerapan manajemen sentimen di media sosial universitas, terutama dengan menggunakan algoritma Naive Bayes dan teknologi v (AI) GPT-40, dalam menganalisis komentarcalon mahasiswa dan stakeholder di Instagram. Penelitian ini bertujuan untuk memahami pola sentimen audiens, yang diklasifikasikan menjadi positif, negatif, dan netral, serta mengidentifikasi gaya bahasa yang kompleks sepertiironi, sindiran, sarkasme, satire, dan metafora. Algoritma Naive Bayes digunakan untuk pengklasifikasian sentimendasar, sedangkanAIGPT-40 berperan dalam menganalisis makna lebih dalam dari gaya bahasa yang tidak eksplisit, sehingga meningkatkan akurasi klasifikasi dan mengoptimalkan interpretasi sentimen. Business Intelligence (BI) digunakan untuk menyediakan visualisasi data dan mendukung pengambilan keputusan yang cepat. Temuan penelitian menunjukkan bahwa pendekatan yang memperhatikan konteks budaya dan linguistik menghasilkanpemahaman sentimen yang lebih akurat dan memberikan wawasan penting dalam strategi branding universitas. Kombinasi teknologi Machine learning dan AIdalam analisis sentimen memberikan kontribusipada pengembangan model manajemen sentimen yang lebih responsif dan relevan dengan kebutuhan branding universitas di era digital.  \nKata Kunci – Manajemen Sentimen, Media Sosial, Machine learning, AI GPT-40, Naive Bayes, Gaya Bahasa, Business Intelligence  \nOptimizing Sentiment Management on University Social Media through Machine learning and AI: A Case Study on Instagram Comments  \nABSTRACT– This study explores the effectiveness of sentiment management on university social media, particularly through the use of the Naive Bayes algorithm and GPT-40 artificial intelligence (AI) technology, in analyzing comments from prospective students and stakeholders on Instagram. The research aims to understand audience sentiment patterns, classified into positive, negative, and neutral, while also identifying complex linguistic styles such as irony, innuendo, sarcasm, satire, and metaphor. The Naive Bayes algorithm is applied for basic sentiment classification, while AI GPT-40 plays a role in analyzing the deeper meanings of non-explicit linguistic styles, thereby improving classification accuracy and optimizing sentiment interpretation. Business Intelligence (BI) is used to provide data visualization and support rapid decision-making. The study’s findings indicate that an approach attentive to cultural and linguistic context results in more accurate sentiment comprehension and provides critical insights for university branding strategies. The combination of machine learning and AI technologies in sentiment analysis contributes to developing a more responsive and relevant sentiment management model for university branding needs in the digital era.  \nKeywords – Sentiment Management, Social Media, Machine learning, AI GPT-40, Naive Bayes, Linguistic Style, Business Intelligence  \nThis is an open access article under the CC BY-SA license  \nTersedia di [https://ojs.unikom.ac.id/index.php/JTK3TI](https://ojs.unikom.ac.id/index.php/JTK3TI)  \nSri Pernia Warakmulty, dan Yeffry Handoko Putra JTK3TI: Jurnal Tata Kelola dan Kerangka Kerja TI, Vol. 11, No. 1, Mei 2025  \n1. PENDAHULUAN  \nPendidikan memiliki peranan yang sangat penting dalam meningkatkan kompetensisumber daya manusia. Melalui pendidikan, kecerdasan intelektual, sikap, dan keterampilan seseorang dapat di","cbCaipJiYcVn6nd1","https://ap.wps.com/l/cbCaipJiYcVn6nd1","pdf",667076,8,"Indonesian","# Pendahuluan\n## Peran pendidikan dan indikator kemajuan bangsa\n## Pertumbuhan perguruan tinggi dan persaingan menarik calon mahasiswa\n## Kebutuhan branding dan pengelolaan sentimen yang efektif\n## Pengembangan model manajemen sentimen berbasis machine learning","[{\"question\":\"Penelitian ini menggunakan pendekatan apa untuk mengelola sentimen di media sosial universitas?\",\"answer\":\"Penelitian menggunakan manajemen sentimen berbasis algoritma Naive Bayes dan teknologi AI GPT-40 untuk menganalisis komentar di Instagram.\"},{\"question\":\"Bagaimana sentimen diklasifikasikan dalam penelitian ini?\",\"answer\":\"Sentimen diklasifikasikan menjadi positif, negatif, dan netral, dengan penekanan pada interpretasi makna dari gaya bahasa yang tidak eksplisit.\"},{\"question\":\"Mengapa konteks budaya dan linguistik penting dalam analisis sentimen pada penelitian ini?\",\"answer\":\"Pendekatan yang memperhatikan konteks budaya dan linguistik menghasilkan pemahaman sentimen yang lebih akurat dan memberi wawasan untuk strategi branding universitas.\"}]","Optimalisasi Manajemen Sentimen di Media Sosial Universitas melalui Machine Learning dan AI - Studi Kasus pada Komentar Instagram - Jurnal | PDF",12]