[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-id-113":3,"doc-seo-193941-113":41,"doc-detail-193941-id":114},{"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":107,"head_meta":109,"extra_data":111,"updated_unix":113},113,"id","accuracy-of-sentiment-analysis-clickbait-detection","Accuracy Of Sentiment Analysis: Clickbait Detection","","Dokumen ini menyajikan hasil analisis sentimen yang berfokus pada deteksi clickbait, disajikan dalam format tabel dan grafik word cloud. Tabel-tabel tersebut menampilkan matriks kebingungan (confusion matrix) untuk berbagai skenario deteksi, membandingkan antara hasil aktual dan prediksi untuk kelas positif/negatif serta non-clickbait/clickbait. Diberikan pula perbandingan hasil setelah preprocessing, baik tanpa maupun dengan stopwords, yang mengukur metrik seperti presisi, recall, F1-score, dan akurasi untuk setiap kelas. Grafik word cloud pertama menampilkan kata-kata kunci terkait dengan topik Indonesia, demo, Jokowi, DPR, Habidie, dan polisi, yang mengindikasikan konteks politik atau sosial. Grafik word cloud kedua menunjukkan kata-kata kunci seperti Habidie, UU, KPK, sinopsis, sinetron, drama, dan kabut asap, yang menyiratkan fokus pada konten hiburan, hukum, dan peristiwa terkini. Analisis ini bertujuan untuk mengevaluasi efektivitas model dalam mengklasifikasikan konten sebagai clickbait atau bukan berdasarkan fitur-fitur yang diekstraksi dari teks.",{"@graph":51,"@context":106},[52,68,89],{"@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":39,"@type":58,"position":64},"https://docshare.wps.com/id/template/umum/",3,{"item":66,"name":47,"@type":58,"position":67},"https://docshare.wps.com/id/template/accuracy-of-sentiment-analysis-clickbait-detection/193941/",4,{"url":66,"name":47,"@type":69,"image":70,"author":75,"headline":47,"publisher":78,"fileFormat":81,"inLanguage":45,"description":49,"dateModified":82,"datePublished":83,"encodingFormat":81,"isAccessibleForFree":84,"interactionStatistic":85},"DigitalDocument",{"url":71,"@type":72,"width":73,"height":74},"https://docshare.wps.com/thumbnails/accuracy-of-sentiment-analysis-clickbait-detection/193941.png","ImageObject",442,249,{"name":76,"@type":77},"eBook King","Person",{"url":56,"name":79,"@type":80},"DocShare","Organization","application/pdf","2026-09-19","2026-09-03",true,{"@type":86,"interactionType":87,"userInteractionCount":9},"InteractionCounter",{"@type":88},"ViewAction",{"@type":90,"mainEntity":91},"FAQPage",[92,98,102],{"name":93,"@type":94,"acceptedAnswer":95},"Apa yang dimaksud dengan matriks kebingungan dalam konteks analisis ini?","Question",{"text":96,"@type":97},"Matriks kebingungan adalah tabel yang digunakan untuk menggambarkan kinerja model klasifikasi. Tabel ini membandingkan nilai aktual dengan nilai prediksi, menunjukkan True Positives (TP), False Positives (FP), True Negatives (TN), dan False Negatives (FN).","Answer",{"name":99,"@type":94,"acceptedAnswer":100},"Bagaimana perbedaan hasil deteksi clickbait antara menggunakan stopwords dan tanpa menggunakan stopwords?",{"text":101,"@type":97},"Hasil menunjukkan bahwa penggunaan stopwords dalam preprocessing teks menghasilkan akurasi yang sedikit lebih tinggi (77%) dibandingkan tanpa stopwords (72%) untuk klasifikasi clickbait/non-clickbait. Presisi dan recall untuk kedua kelas juga cenderung lebih baik dengan stopwords.",{"name":103,"@type":94,"acceptedAnswer":104},"Apa saja topik utama yang muncul dalam grafik word cloud pertama dan kedua?",{"text":105,"@type":97},"Grafik word cloud pertama didominasi oleh kata-kata seperti Indonesia, demo, Jokowi, DPR, Habidie, dan polisi, menunjukkan fokus pada isu politik dan sosial. Grafik kedua menyoroti kata-kata seperti Habidie, UU, KPK, sinopsis, sinetron, dan drama, yang mengindikasikan konten terkait hiburan dan hukum.","https://schema.org",{"og:url":66,"og:type":108,"og:title":47,"og:site_name":79,"og:description":49},"article",{"robots":110,"canonical":66},"index,follow",{"doc_id":112,"site_id":44},193941,1788435001,{"code":4,"msg":5,"data":115},{"doc_id":112,"user_id":116,"nickname":76,"user_avatar":117,"doc_module":9,"category_id":38,"category_name":39,"doc_title":47,"doc_description":49,"doc_content":118,"file_id":119,"file_url":120,"file_type":121,"file_size":122,"view_count":4,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":123,"language":124,"language_code":45,"site_id":44,"html_lang":45,"table_of_contents":125,"faqs":126,"seo_title":127,"seo_description":49,"update_tm":113,"read_time":64},962088006270,"https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","| Aktual | Prediksi |  |\n| --- | --- | --- |\n|  | Nilai Positif | Nilai Negatif |\n| Nilai Positif | TP | FN |\n| Nilai Negatif | FP | TN |\n\n| Aktual | Prediksi |  |\n| --- | --- | --- |\n|  | Non-clickbait | Clickbait |\n| Non-clickbait | 1.427 | 315 |\n| Clickbait | 525 | 733 |\n\n\n| Aktual | Prediksi |  |\n| --- | --- | --- |\n|  | Non-clickbait | Clickbait |\n| Non-clickbait | 1.509 | 233 |\n| Clickbait | 472 | 786 |\n\n\n| Preprocessi\u003Cbr>ng | Label | Presi\u003Cbr>si | Recall | F1-\u003Cbr>Score | Akura\u003Cbr>si |\n| --- | --- | --- | --- | --- | --- |\n| Tanpa\u003Cbr>Stopwords | Non\u003Cbr>clickbait | 73% | 82% | 77% | 72% |\n|  | Clickbait | 70% | 58% | 64% |  |\n| Dengan\u003Cbr>Stopwords | Non\u003Cbr>clickbait | 76% | 87% | 81% | 77% |\n|  | Clickbait | 77% | 62% | 69% |  |","cbCaif7wGkAhbLdu","https://ap.wps.com/l/cbCaif7wGkAhbLdu","pdf",924312,7,"Indonesian","# Matriks Kebingungan\n## Konfigurasi Tanpa Stopwords\n## Konfigurasi Dengan Stopwords\n# Grafik Word Cloud","[{\"question\":\"Apa yang dimaksud dengan matriks kebingungan dalam konteks analisis ini?\",\"answer\":\"Matriks kebingungan adalah tabel yang digunakan untuk menggambarkan kinerja model klasifikasi. Tabel ini membandingkan nilai aktual dengan nilai prediksi, menunjukkan True Positives (TP), False Positives (FP), True Negatives (TN), dan False Negatives (FN).\"},{\"question\":\"Bagaimana perbedaan hasil deteksi clickbait antara menggunakan stopwords dan tanpa menggunakan stopwords?\",\"answer\":\"Hasil menunjukkan bahwa penggunaan stopwords dalam preprocessing teks menghasilkan akurasi yang sedikit lebih tinggi (77%) dibandingkan tanpa stopwords (72%) untuk klasifikasi clickbait/non-clickbait. Presisi dan recall untuk kedua kelas juga cenderung lebih baik dengan stopwords.\"},{\"question\":\"Apa saja topik utama yang muncul dalam grafik word cloud pertama dan kedua?\",\"answer\":\"Grafik word cloud pertama didominasi oleh kata-kata seperti Indonesia, demo, Jokowi, DPR, Habidie, dan polisi, menunjukkan fokus pada isu politik dan sosial. Grafik kedua menyoroti kata-kata seperti Habidie, UU, KPK, sinopsis, sinetron, dan drama, yang mengindikasikan konten terkait hiburan dan hukum.\"}]","Accuracy Of Sentiment Analysis: Clickbait Detection | PDF"]