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All Journal KOMPUTA : Jurnal Ilmiah Komputer dan Informatika An-Nahdlah: Jurnal Pendidikan Islam Al-Ihda' : Jurnal Pendidikan dan Pemikiran Jurnal Riset Hukum, Ekonomi Islam, ekonomi, Manajemen dan Akuntansi Jurnal Kreasi Rakyat BULLET : Jurnal Multidisiplin Ilmu Jurnal Pengabdian Nasional (JPN) Indonesia
Azlina
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Journal : KOMPUTA : Jurnal Ilmiah Komputer dan Informatika

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Optimasi Augmentasi Data Berbasis Synonym Replacement pada Klasifikasi Teks Berita Menggunakan Neural Network Huriah, Iffah Risma; Widianingrum, Amelia Ismania Sita; Azlina; Taslim
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 1 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i1.15339

Abstract

In the digital era, online news has become one of the primary sources of information, encompassing various categories such as politics, technology, entertainment, and business. The increasing volume of news poses challenges in organizing and categorizing information into relevant categories. This study aims to enhance the accuracy of news text classification through a data augmentation approach based on synonym replacement. The methods employed include text preprocessing for data cleaning, augmentation using synonym replacement to improve data diversity, feature representation using TF-IDF and Word2Vec, and modeling with Neural Networks. Evaluation metrics such as accuracy, precision, recall, and F1-score were used to assess performance. The results indicate that data augmentation can improve model accuracy by up to 95%, with balanced training and validation data distributions. The confusion matrix shows that most data can be correctly classified, although some errors occur in categories with similar features. This study demonstrates that synonym replacement-based data augmentation is effective in improving news text classification performance, particularly for datasets with limited training data.
Co-Authors Agus Wahid Suyoto Agustina Aini, Zahriatul Akbar, Indra Amalia, Cut Intan Azizah FARIDAH H, Khairita Haris Riadi Huriah, Iffah Risma Lisnawati Marlizayani Meysinta, Desti Putri Mirnawati Mirnawati Najibullah Nasrun Harahap Puspita, Desy Rahmad Ricky Ariansyah Saiful Bahgia Sufika, Arwina SULASTRI Taslim Widianingrum, Amelia Ismania Sita