[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125129-id":3,"doc-seo-125129-113":31,"detail-sidebar-cat-0-id-113":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125129,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",54,"Penelitian & Laporan","ANALISIS SENTIMEN OPINI DEBAT CALON PRESIDEN INDONESIA 2024 - STUDI KASUS PADA DATA TWITTER 2024","Penelitian ini menganalisis sentimen opini publik terhadap Debat Calon Presiden Indonesia 2024 melalui data Twitter 2024 dengan lima algoritma klasifikasi: Naïve Bayes, Decision Tree, Support Vector Machine, Random Forest, dan K-Nearest Neighbors. Twitter API digunakan untuk mengumpulkan 1.300 tweet sebagai objek penelitian. Tahap analisis meliputi ekstraksi teks dan preprocessing berupa pembersihan data, tokenisasi, stemming, serta penghapusan stopwords. Hasil menunjukkan 51,55% positif, 14,83% negatif, dan 34,21% netral. SVM dan Random Forest mencapai akurasi tertinggi 81%, sedangkan Naïve Bayes terendah 62%.","ANALISIS SENTIMEN OPINI DEBAT CALON PRESIDEN DENGAN MENGGUNAKAN CLASSIFIER MACHINE LEARNING  \n(STUDI KASUS: PADA DATA TWITTER 2024)  \nRini Widaswari Purba1, Arya Adyaksa Waskita2*, Makshun3  \n1,2,3Program Studi Magister Teknik Informatika, Universitas Pamulang Tangerang Selatan, Indonesia.  \nCorrespondence email: [aawaskita@unpam.ac.id](aawaskita@unpam.ac.id)  \nArticle history: Submission date: August-9-2024 Revised date: August-20-2024 Published date: November-30-2024   \nABSTRACT  \nThis study aims to analyze public sentiment towards the Indonesian 2024 Presidential Debate using five Machine Learning classification algorithms: Naïve Bayes, Decision Tree, Support Vector Machine, Random Forest, and KNearest Neighbors. The data used in this research was sourced from Twitter, a major social media platform with a large and diverse volume of data. The research object is public opinions expressed on Twitter, with the subject of the research being tweets collected using the Twitter API, resulting in 1,300 data points. Data analysis involves text extraction and preprocessing, including data cleaning, tokenization, stemming, and stopword removal. The research results show the following sentiment distribution: 51.55% positive (663 tweets), 14.83% negative (183 tweets), and 34.21% neutral (440 tweets). Among the models, Support Vector Machine and Random Forest demonstrated the highest performance with an accuracy of 81%, while Naïve Bayes had the lowest performance with an accuracy of 62%. Despite variations in performance among the algorithms used, no single method was consistently effective in sentiment classification. This research contributes to mapping public sentiment related to political debates in Indonesia through social media data analysis and provides insights into the effectiveness of Machine Learning algorithms in sentiment analysis.  \nKeywords: Sentiment Analysis, 2024 Presidential Debate, Machine Learning, Twitter, Public Opinion.  \nABSTRAK  \nPenelitian ini bertujuan untuk menganalisis sentimen opini masyarakat terhadap Debat Calon Presiden Indonesia 2024 menggunakan lima algoritma klasifikasi Machine Learning: Naïve Bayes, Decision Tree, Support Vector Machine, Random Forest, dan K-Nearest Neighbors. Data yang digunakan dalam penelitian ini diambil dari Twitter, yang merupakan salah satu platform media sosial dengan volume data yang besar dan beragam. Objek penelitian ini adalah opini publik yang diekspresikan di Twitter, dengan subjek penelitian berupa tweet yang diambil menggunakan Twitter API, menghasilkan 1300 data poin. Analisis data melibatkan proses ekstraksi teks dan preprocessing yang mencakup pembersihan data, tokenisasi, stemming, dan penghapusan stopwords. Hasil penelitian menunjukkan distribusi sentimen sebagai berikut: 51,55% positif (663 tweet), 14,83% negatif (183 tweet), dan 34,21% netral (440 tweet) . Dari hasil pemodelan, Support Vector Machine dan Random Forest menunjukkan performa tertinggi denganakurasi 81%, sedangkan Naïve Bayes memiliki performa paling rendah dengan akurasi 62% . Meskipun terdapatvariasi kinerja di antara algoritma yang di gunakan, tidak ada satu metode pun yang sepenuhnya konsisten dalamklasifikasi sentimen. Penelitian ini memberikan kontribusi dalam memetakan sentimen publik terkait perdebatan politik di Indonesia melalui analisis data media sosial. serta memberikan wawasan tentang efektivitas algoritma Machine Learning dalam analisis sentimen.  \nKata Kunci: Analisis Sentimen, Debat Capres 2024, Machine Learning, Twitter, Opini Publik.  \nPENDAHULUAN  \nPemilu Presiden Indonesia tahun 2024, sering disebut Pilpres 2024, adalah pemilihan demokratis kelima di negara tersebut untuk memilih Presiden dan Wakil  \nPresiden Republik Indonesia. Kegiatan ini bertujuan untuk menetapkan kepemimpinan baru yang akan bertanggung jawab atas posisi presiden dan wakilpresiden selama periode 2024-2029, dengan haripemungutan suara ditetapkan pada Rabu, 14 Februari 2024. Event ini diadakan sebagai arena ","cbCaibPStDq2CvnQ","https://ap.wps.com/l/cbCaibPStDq2CvnQ","pdf",1483261,5,1,12,"Indonesian","id",113,"# PENDAHULUAN\n## Konteks Pemilu Presiden Indonesia 2024\n## Profil Calon dan Pelaksanaan Debat Capres-Cawapres\n# METODOLOGI PENELITIAN\n## Pengumpulan Data Twitter dengan Twitter API\n## Preprocessing Teks: Pembersihan, Tokenisasi, Stemming, Stopword Removal\n# HASIL DAN PEMBAHASAN\n## Distribusi Sentimen Positif, Negatif, Netral\n## Performa Model Klasifikasi dan Akurasi","[{\"question\":\"Penelitian ini menggunakan algoritma Machine Learning apa saja untuk analisis sentimen?\",\"answer\":\"Penelitian menggunakan Naïve Bayes, Decision Tree, Support Vector Machine, Random Forest, dan K-Nearest Neighbors.\"},{\"question\":\"Berapa jumlah data tweet yang digunakan dan dari mana datanya diperoleh?\",\"answer\":\"Data berasal dari Twitter dan dikumpulkan menggunakan Twitter API, menghasilkan 1.300 tweet.\"},{\"question\":\"Bagaimana distribusi sentimen yang ditemukan pada data Twitter 2024?\",\"answer\":\"Distribusi sentimen menunjukkan 51,55% positif, 14,83% negatif, dan 34,21% netral.\"}]","ANALISIS SENTIMEN OPINI DEBAT CALON PRESIDEN INDONESIA 2024 - 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