[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-id-113":3,"doc-seo-193939-113":41,"doc-detail-193939-id":112},{"code":4,"msg":5,"data":6},0,"success",[7,13,17,21,25,29,33,37],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":4,"slug":12},178,1,"Template","Faktur","faktur",{"id":14,"doc_module":9,"doc_module_name":10,"category_name":15,"show_sort_weight":4,"slug":16},192,"Formulir","formulir-192",{"id":18,"doc_module":9,"doc_module_name":10,"category_name":19,"show_sort_weight":4,"slug":20},180,"Media Sosial","media-sosial",{"id":22,"doc_module":9,"doc_module_name":10,"category_name":23,"show_sort_weight":4,"slug":24},179,"Poster","poster",{"id":26,"doc_module":9,"doc_module_name":10,"category_name":27,"show_sort_weight":4,"slug":28},176,"Presentasi","presentasi",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":4,"slug":32},177,"Resume","resume",{"id":34,"doc_module":9,"doc_module_name":10,"category_name":35,"show_sort_weight":4,"slug":36},182,"Surat","surat-a95d00d3aaf04f3b854ecf140f00d385",{"id":38,"doc_module":9,"doc_module_name":10,"category_name":39,"show_sort_weight":4,"slug":40},183,"Umum","umum-07d1ff437201438088836b2b1ed3c90f",{"code":4,"msg":42,"data":43},"ok",{"site_id":44,"language":45,"slug":46,"title":47,"keywords":48,"description":49,"schema_data":50,"social_meta":105,"head_meta":107,"extra_data":109,"updated_unix":111},113,"id","riggs-script-and-sentiment-analysis-results-of-news","RIGGS - Naskah dan Hasil Analisis Sentimen Berita","","Analisis sentimen berbasis berita dilakukan untuk mengevaluasi polaritas Negatif, Positif, dan Netral. Data awal menunjukkan distribusi sentimen dengan total 300, lalu mengalami perubahan setelah augmentasi menjadi total 363, dengan peningkatan jumlah pada kategori Positif dan Netral. Kinerja model dinilai menggunakan Accuracy dan F1 Score pada berbagai skema split (90:10, 80:20, 70:30) serta beberapa algoritma seperti SVM (LinearSVC), MultinomialNB, dan Logistic Regression. Hasil klasifikasi pada data contoh memperlihatkan prediksi untuk topik seperti kenaikan BBM dan kemenangan Timnas.",{"@graph":51,"@context":104},[52,68,83],{"@type":53,"itemListElement":54},"BreadcrumbList",[55,59,62,65],{"item":56,"name":57,"@type":58,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":60,"name":10,"@type":58,"position":61},"https://docshare.wps.com/id/template/",2,{"item":63,"name":27,"@type":58,"position":64},"https://docshare.wps.com/id/template/presentasi/",3,{"item":66,"name":47,"@type":58,"position":67},"https://docshare.wps.com/id/template/riggs-script-and-sentiment-analysis-results-of-news/193939/",4,{"url":66,"name":47,"@type":69,"author":70,"headline":47,"publisher":73,"fileFormat":76,"inLanguage":45,"description":49,"dateModified":77,"datePublished":77,"encodingFormat":76,"isAccessibleForFree":78,"interactionStatistic":79},"DigitalDocument",{"name":71,"@type":72},"Stanley","Person",{"url":56,"name":74,"@type":75},"DocShare","Organization","application/pdf","2026-09-03",true,{"@type":80,"interactionType":81,"userInteractionCount":4},"InteractionCounter",{"@type":82},"ViewAction",{"@type":84,"mainEntity":85},"FAQPage",[86,92,96,100],{"name":87,"@type":88,"acceptedAnswer":89},"Apa perbedaan distribusi sentimen sebelum dan sesudah augmentasi?","Question",{"text":90,"@type":91},"Sebelum augmentasi total sentimen 300, sedangkan setelah augmentasi total menjadi 363. Kategori Netral meningkat, dan Positif juga bertambah setelah augmentasi.","Answer",{"name":93,"@type":88,"acceptedAnswer":94},"Model apa saja yang digunakan dan bagaimana cara menilai performanya?",{"text":95,"@type":91},"Model yang digunakan mencakup SVM (LinearSVC), MultinomialNB, dan Logistic Regression. Penilaian dilakukan melalui Accuracy dan F1 Score pada beberapa skema split data.",{"name":97,"@type":88,"acceptedAnswer":98},"Bagaimana hasil evaluasi pada skema split 90:10, 80:20, dan 70:30?",{"text":99,"@type":91},"Pada setiap skema split, nilai Accuracy dan F1 Score dihitung untuk membandingkan performa antar model. Performa cenderung berbeda tergantung algoritma dan proporsi data latih-uji.",{"name":101,"@type":88,"acceptedAnswer":102},"Bagaimana contoh prediksi sentimen pada judul berita tertentu?",{"text":103,"@type":91},"Judul “Kenaikan Harga BBM Membuat Masyarakat Resah” diprediksi Negatif, sedangkan “Timnas Indonesia Menang Telak 4-0 Lawan Thailand” diprediksi Positif. Matriks prediksi vs aktual menunjukkan tingkat kecocokan yang tinggi.","https://schema.org",{"og:url":66,"og:type":106,"og:title":47,"og:site_name":74,"og:description":49},"article",{"robots":108,"canonical":66},"index,follow",{"doc_id":110,"site_id":44},193939,1788434968,{"code":4,"msg":5,"data":113},{"doc_id":110,"user_id":114,"nickname":71,"user_avatar":115,"doc_module":9,"category_id":26,"category_name":27,"doc_title":47,"doc_description":49,"doc_content":116,"file_id":117,"file_url":118,"file_type":119,"file_size":120,"view_count":4,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":121,"language":122,"language_code":45,"site_id":44,"html_lang":45,"table_of_contents":123,"faqs":124,"seo_title":125,"seo_description":49,"update_tm":111,"read_time":67},2336477405376,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","| Judul | Judul setelah pembersihan teks |\n| --- | --- |\n| Menteri LHAkanAmbil Langkah Hukum Soal Tambang Nikel di Raja Ampat | menteri lh ambil langkah hukum tambang nikel raja ampat |\n| Prabowo Puji Polri Bisa Panen Raya Jagung di Bengkayang Kalbar | prabowo puji polri panen rayajagung bengkayang kalbar |\n| Kapolri: Panen Jagung Serentak Capai 2,54 Juta Ton | kapolri panen jagung serentak capaijuta ton |\n| Adu Cepat BMW Vs Whoosh di Tol MBZ Bikin Geger | adu cepat bmw vs whoosh tol mbz bikin geger |\n\n\n| Sentimen | Distribusi awal | Distribusi setelah Augmentasi |\n| --- | --- | --- |\n| Negatif | 121 | 121 |\n| Positif | 113 | 121 |\n| Netral | 64 | 121 |\n| Total | 300 | 363 |\n\n\n| Split | Model | Accuracy | F1 Score |\n| --- | --- | --- | --- |\n| 90:10 | SVM (LinearSVC) | 89.19 | 87.68 |\n| 80:20 | SVM (LinearSVC) | 90.41 | 89.94 |\n| 70:30 | SVM (LinearSVC) | 89.91 | 89.59 |\n| 90:10 | MultinomialNB | 81.08 | 76.66 |\n| 80:20 | MultinomialNB | 83.56 | 80.44 |\n| 70:30 | MultinomialNB | 77.98 | 74.60 |\n| 90:10 | Logistic Regression | 75.68 | 71.37 |\n| 80:20 | Logistic Regression | 80.82 | 78.04 |\n\n\n| 90:10 | MultinomialNB | 89.19 | 89.28 |\n| --- | --- | --- | --- |\n| 80:20 | MultinomialNB | 90.41 | 90.34 |\n| 70:30 | MultinomialNB | 93.58 | 93.58 |\n| 90:10 | Logistic Regression | 91.89 | 91.94 |\n| 80:20 | Logistic Regression | 90.41 | 90.4 |\n| 70:30 | Logistic Regression | 91.74 | 91.79 |\n\n\n| Split | Model | Accuracy | F1 Score |\n| --- | --- | --- | --- |\n| 90:10 | SVM (LinearSVC) | 89.19 | 89.03 |\n| 80:20 | SVM (LinearSVC) | 87.67 | 87.7 |\n| 70:30 | SVM (LinearSVC) | 88.07 | 88.01 |\n| 90:10 | MultinomialNB | 89.19 | 89.11 |\n| 80:20 | MultinomialNB | 87.67 | 87.86 |\n\n\n| Judul | Sentimen prediksi |\n| --- | --- |\n| Kenaikan Harga BBM Membuat Masyarakat Resah | Negatif |\n| Timnas Indonesia Menang Telak 4-0 Lawan Thailand | Positif |\n\n\n|  | Precision | recall | F1-score | Support |\n| --- | --- | --- | --- | --- |\n| Negatif | 1.00 | 0.97 | 0.98 | 32 |\n| Netral | 1.00 | 1.00 | 1.00 | 44 |\n| Positif | 0.97 | 1.00 | 0.99 | 33 |\n| Accuracy |  |  | 0.99 | 109 |\n| Macro avg | 0.99 | 0.99 | 0.99 | 109 |\n\n| Prediksi / Aktual | Negatif | Netral | Positif |\n| --- | --- | --- | --- |\n| Negatif | 31 | 0 | 1 |\n| Netral | 0 | 44 | 0 |\n| Positif | 0 | 0 | 33 |","cbCaidK160N0FZzP","https://ap.wps.com/l/cbCaidK160N0FZzP","pdf",493694,10,"Indonesian","# Ringkasan Judul Berita\n# Analisis Sentimen dan Distribusi\n## Sentimen Awal vs Setelah Augmentasi\n# Evaluasi Model\n## Split Data (90:10, 80:20, 70:30)\n## Perbandingan Akurasi dan F1 Score\n# Prediksi Sentimen pada Contoh\n## Kenaikan BBM vs Resah\n## Timnas Indonesia vs Thailand\n# Matriks Prediksi vs Aktual","[{\"question\":\"Apa perbedaan distribusi sentimen sebelum dan sesudah augmentasi?\",\"answer\":\"Sebelum augmentasi total sentimen 300, sedangkan setelah augmentasi total menjadi 363. Kategori Netral meningkat, dan Positif juga bertambah setelah augmentasi.\"},{\"question\":\"Model apa saja yang digunakan dan bagaimana cara menilai performanya?\",\"answer\":\"Model yang digunakan mencakup SVM (LinearSVC), MultinomialNB, dan Logistic Regression. Penilaian dilakukan melalui Accuracy dan F1 Score pada beberapa skema split data.\"},{\"question\":\"Bagaimana hasil evaluasi pada skema split 90:10, 80:20, dan 70:30?\",\"answer\":\"Pada setiap skema split, nilai Accuracy dan F1 Score dihitung untuk membandingkan performa antar model. Performa cenderung berbeda tergantung algoritma dan proporsi data latih-uji.\"},{\"question\":\"Bagaimana contoh prediksi sentimen pada judul berita tertentu?\",\"answer\":\"Judul “Kenaikan Harga BBM Membuat Masyarakat Resah” diprediksi Negatif, sedangkan “Timnas Indonesia Menang Telak 4-0 Lawan Thailand” diprediksi Positif. Matriks prediksi vs aktual menunjukkan tingkat kecocokan yang tinggi.\"}]","RIGGS - Naskah dan Hasil Analisis Sentimen Berita | PDF"]