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All Journal Bulletin of Electrical Engineering and Informatics JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Sistem Informasi Kaputama (JSIK) JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Ilmiah MADIYA: Masyarakat Mandiri Berkarya Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Journal of Vocational Instruction Komposit : Jurnal Pengabdian Dan Pemberdayaan Masyarakat
Julham Julham
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Mechanical Engineering Social Sciences Other

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Journal : Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI)

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Perbandingan TextRank Berbasis TF-IDF dan Word2Vec dalam Peringkasan Teks Berita Bahasa Indonesia Gurning, Yohannes Christian; Saragih, Samuel Cristian; Lase, Yuyun Yusnida; Julham, Julham
Jurnal Komputer Teknologi Informasi Sistem Informasi (JUKTISI) Vol. 4 No. 2 (2025): September 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i2.552

Abstract

Automatic text summarization has become an essential solution for processing massive textual information, particularly in lengthy news articles. This study compares two variants of the TextRank algorithm using different weighting schemes: TF-IDF and Word2Vec, for summarizing Indonesian news texts. The dataset comprises 160 news articles from Kompas.com, which underwent preprocessing. Evaluation was conducted using ROUGE metrics (ROUGE-1, ROUGE-2, ROUGE-L), manual readability assessment, and execution runtime. The results indicate that TextRank with Word2Vec outperforms TF-IDF in both ROUGE scores (ROUGE-1 F1: 0.7033 vs 0.6454) and processing speed. These findings suggest that incorporating semantic representations into graph-based algorithms like TextRank significantly improves summary quality and runtime efficiency.
Co-Authors A, Azanuddin Adam, Hikmah Adwin Al Khowarizmi Anugerahwaty, Rina Dhitisari, Indri Fawwaz, Mohammad Faris Faza, Sharfina Gurning, Yohannes Christian Harefa, Hafid Rahman Hulu, Fitria Nova Husna, Meryatul Idkhan, A. Muh Imani, Muhammad Rayyan Lubis, Arif Ridho Muharman Lubis Nasution, Tuti Adi Tama Nurlinda Nurlinda, Nurlinda Prayudani, Santi Putra, Purwa Hasan Raditiansyah, Farhan Rina Anugrahwaty Romi Fadillah Rahmat Samnur . Saragih, Samuel Cristian Selvida, Desilia Sembiring, Ajulio Padly Yusuf, Kadri Yuyun Yusnida Lase