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Berbagai metrik evaluasi seperti Akurasi dan Kappa Statistics dibahas secara rinci, termasuk rumus dan interpretasinya. Tabel yang disajikan memberikan gambaran visual mengenai perbedaan klasifikasi, dengan penekanan pada True Positive (TP), False Negative (FN), False Positive (FP), dan True Negative (TN). Analisis tren bulanan ulasan saham untuk periode November 2021 hingga Februari 2022 juga divisualisasikan dalam bentuk grafik, menunjukkan fluktuasi sentimen pasar. Selain itu, dokumen ini membandingkan bobot kata-kata kunci (terms) dalam ulasan pertama, memberikan wawasan tentang terminologi yang sering muncul dan pengaruhnya terhadap sentimen. Evaluasi model berdasarkan 'Cost' dan Akurasi juga menjadi fokus, menampilkan perbandingan antara model Support Vector Machine (SVM) dan Maximum Entropy. Matriks prediksi yang merinci hasil klasifikasi aktual vs prediksi dari kedua model memberikan dasar kuat untuk perbandingan kinerja. Data yang disajikan mengindikasikan bahwa model SVM secara konsisten menunjukkan kinerja yang lebih unggul dibandingkan Maximum Entropy dalam tugas analisis ulasan saham ini, baik dari segi Akurasi maupun Kappa Statistics.",{"@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/automatic-text-classification-model-performance-measurement-for-stock-review-analysis/180896/",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/automatic-text-classification-model-performance-measurement-for-stock-review-analysis/180896.png","ImageObject",300,407,{"name":89,"@type":90},"Pentious","Person",{"url":68,"name":92,"@type":93},"DocShare","Organization","application/pdf","2026-10-04","2026-09-02",true,{"@type":99,"interactionType":100,"userInteractionCount":102},"InteractionCounter",{"@type":101},"ViewAction",6,{"@type":104,"mainEntity":105},"FAQPage",[106,112,116],{"name":107,"@type":108,"acceptedAnswer":109},"Apa saja metrik utama yang digunakan untuk mengevaluasi kinerja model klasifikasi teks dalam dokumen ini?","Question",{"text":110,"@type":111},"Dokumen ini menggunakan metrik utama seperti Akurasi dan Kappa Statistics untuk mengevaluasi kinerja model klasifikasi teks, yang masing-masing dihitung berdasarkan perbandingan antara prediksi model dan data aktual.","Answer",{"name":113,"@type":108,"acceptedAnswer":114},"Bagaimana cara kerja model klasifikasi teks otomatis dalam menganalisis ulasan saham?",{"text":115,"@type":111},"Model klasifikasi teks otomatis menganalisis ulasan saham dengan mempelajari pola dan kata kunci yang terkait dengan sentimen positif atau negatif, sehingga dapat mengklasifikasikan ulasan baru berdasarkan pembelajaran tersebut.",{"name":117,"@type":108,"acceptedAnswer":118},"Model machine learning mana yang menunjukkan kinerja lebih baik dalam analisis ulasan saham menurut dokumen ini?",{"text":119,"@type":111},"Menurut dokumen ini, model Support Vector Machine (SVM) menunjukkan kinerja yang lebih unggul dibandingkan model Maximum Entropy dalam analisis ulasan saham, baik dari segi akurasi maupun statistik Kappa.","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},180896,1788342569,{"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},1374404730887,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","| Class | Positive | Negative |\n| --- | --- | --- |\n| Positive | True Positive (TP) | False Negative (FN) |\n| Negative | False Positive (FP) | True Negative (TN) |\n\n\n| Ukuran | Rumus |\n| --- | --- |\n| Accuracy | 􀜶􀜲 + 􀜶􀜰\u003Cbr>(􀜶􀜲 + 􀜨􀜲 + 􀜶􀜰 + 􀜨􀜰) |\n| Kappa\u003Cbr>Statistics | 􀜲0 = 􀜣􀜿􀜿􀝑􀝎􀜽􀜿􀝕\u003Cbr>􀜲􀯖 = [(􀜶􀜲􀜶􀜨􀜽􀝈􀜲~~ ~~) (􀜶􀜲􀜶􀜨􀜽􀝈􀜰~~ ~~ )] + [(􀜨􀜰􀜶􀜶􀜽􀝈􀜰~~ ~~ ) (􀜨􀜲􀜶􀜶􀜽􀝈􀜰~~ ~~ )]􀜭􀜽􀝌􀝌􀜽 􀜵􀝐􀜽􀝐􀝅􀝏􀝐􀝅􀜿 = 􀜲0(1~~ ~~􀜲􀜲􀯖􀯖) |\n\n\n| 800\u003Cbr>400\u003Cbr>0 | JUMLAH ULASAN AJAIB PER BULAN\u003Cbr>\u003Cbr>November 2021 Desember 2021 Januari 2022 Februari 2022 |\n| --- | --- |\n\n\n| Term | Ulasan ke-1 | Term | Ulasan ke-1 |\n| --- | --- | --- | --- |\n| ajaib | 0,175 | loss | 0,698 |\n| tambah | 0,914 | persentase | 0,782 |\n| gain | 0,830 | saham | 0,204 |\n| hai | 0,747 | tolong | 0,335 |\n| hasil | 0,453 | transaksi | 0,456 |\n| keren | 0,368 | : | : |\n| kolom | 0,637 | investasi | 0 |\n\n\n| 􀢻 | 0 | 0 | 0, 54 |\n| --- | --- | --- | --- |\n|  | 0 | 0, 13 | 1, 27 |\n|  | 0, 1 | 0, 65 | 5,41 |\n|  | 0 | 0 | 0 |\n|  | 0 | 0 | 0 |\n|  | [ ⋮ ]2000×1 | [ ⋮ ]2000×1 | [ ⋮ ]2000×1 |\n| b | -1,000435 | -0,9171324 | 0,07713039 |\n\n\n| Evaluasi Model | Cost |  |  |\n| --- | --- | --- | --- |\n|  | 0,1 | 1 | 10 |\n| Accuracy | 0,7625 | 0,845 | 0,8575 |\n| Kappa | 0 | 0,535 | 0,5807 |\n\n\n| Prediksi | Aktual |  |\n| --- | --- | --- |\n|  | Negatif | Positif |\n| Negatif | 58 | 37 |\n| Positif | 20 | 285 |\n\n\n|  | Support Vector Machine | Maximum Entropy |\n| --- | --- | --- |\n| Accuracy | 85,75% | 83% |\n| Kappa | 58,07% | 50,5% |","cbCaie7l8rS0rVcj","https://ap.wps.com/l/cbCaie7l8rS0rVcj","pdf",867788,12,"Indonesian","# Metrik Evaluasi\n## Akurasi\n## Kappa Statistics\n# Analisis Tren Ulasan Saham\n# Perbandingan Bobot Kata Kunci\n# Evaluasi Model\n## Support Vector Machine\n## Maximum Entropy","[{\"question\":\"Apa saja metrik utama yang digunakan untuk mengevaluasi kinerja model klasifikasi teks dalam dokumen ini?\",\"answer\":\"Dokumen ini menggunakan metrik utama seperti Akurasi dan Kappa Statistics untuk mengevaluasi kinerja model klasifikasi teks, yang masing-masing dihitung berdasarkan perbandingan antara prediksi model dan data aktual.\"},{\"question\":\"Bagaimana cara kerja model klasifikasi teks otomatis dalam menganalisis ulasan saham?\",\"answer\":\"Model klasifikasi teks otomatis menganalisis ulasan saham dengan mempelajari pola dan kata kunci yang terkait dengan sentimen positif atau negatif, sehingga dapat mengklasifikasikan ulasan baru berdasarkan pembelajaran tersebut.\"},{\"question\":\"Model machine learning mana yang menunjukkan kinerja lebih baik dalam analisis ulasan saham menurut dokumen ini?\",\"answer\":\"Menurut dokumen ini, model Support Vector Machine (SVM) menunjukkan kinerja yang lebih unggul dibandingkan model Maximum Entropy dalam analisis ulasan saham, baik dari segi akurasi maupun statistik Kappa.\"}]","Pengukuran Kinerja Model Klasifikasi Teks Otomatis untuk Analisis Ulasan Saham | PDF",18]