Peringkasan Teks Multi Dokumen Berbahasa Indonesia Menggunakan Sentence Scoring dan SVM

Authors

  • Deri Fauzi Universitas Islam Negeri Maulana Malik Ibrahim Malang
  • Zainal Abidin Universitas Islam Negeri Maulana Malik Ibrahim Malang https://orcid.org/0000-0002-9261-4952
  • Fatchurrochman Fatchurrochman Universitas Islam Negeri Maulana Malik Ibrahim Malang

DOI:

https://doi.org/10.62411/tc.v23i1.9648

Keywords:

Peringkasan Teks, Multi Dokumen, SVM, Berita Online

Abstract

Berita online berasal dari berbagai sumber portal berita yang tersedia secara luas di dunia maya. Namun, berita online yang melimpah dapat mengesampingkan detail dan keakuratan berita karena tujuannya untuk memberikan informasi terkini sebanyak mungkin. Ketersediaan berita online di internet dapat menyebabkan penerimaan informasi yang berlebihan, memberikan pemahaman yang kurang jelas mengenai substansi berita tersebut. Oleh karena itu, penting untuk menemukan representasi dokumen berita online guna memahami inti dari berita tersebut. Penelitian ini fokus pada menghasilkan ringkasan berita online multi dokumen dari ekstraksi fitur dan proses klasifikasi menggunakan support vector machine. penelitian ini mengklasifikan berita multi dokumen menggunakan ekstraksi fitur Sentence Scoring dan SVM. Sentence Scoring digunakan untuk input pada metode SVM agar dapat melakukan proses klasifikasi untuk menentukan hasil ringkasan. Hasil pengujian menunjukkan bahwa Fold 3 memberikan hasil terbaik, dengan rata-rata Recall 0.946, Presisi 0.487, dan F-Measure 0.634. ROUGE-1 juga mencapai nilai tertinggi pada Fold 3, yaitu 0.946. Faktor kunci dalam hasil peringkasan adalah proses ekstraksi fitur menggunakan Sentence Scoring dan pelatihan data dengan SVM. Fitur seperti data numerik dan kemiripan antar kalimat berpengaruh signifikan terhadap hasil akhir dari peringkasan.

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Published

2024-06-21

Issue

Vol. 23 No. 1 (2024): Februari 2024

Section

Articles

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