Klasifikasi Artikel Ilmiah Terindeks Garuda dengan metode cGAN dan BERT
DOI:
https://doi.org/10.29407/noe.v9i01.26792Keywords:
Augmentasi Data, cGAN, BERT, Klasifikasi bidang ilmu, Artikel ilmiahAbstract
Jumlah publikasi karya ilmiah terus meningkat, mencapai sekitar 2,6 hingga 3 juta artikel setiap tahun. Peningkatan ini menimbulkan tantangan besar dalam otomatisasi proses pengelompokan dan klasifikasi bidang ilmu, terutama karena distribusi data antar kelas yang tidak merata membuat proses klasifikasi menjadi semakin kompleks. Untuk mengatasi hal ini, penelitian ini menggabungkan dua pendekatan yaitu Conditional Generative Adversarial Network (cGAN) dan Bidirectional Encoder Representations from Transformers (BERT). Model cGAN dimanfaatkan untuk menciptakan data sintetis bagi kelas minoritas dengan pendekatan TF-IDF, sementara BERT digunakan untuk klasifikasi berbasis pemahaman konteks. Data hasil sintesis kemudian digabung dengan data asli dan diuji menggunakan metrik seperti akurasi, presisi, recall, dan F1-score. Hasilnya, metode ini mampu mencapai akurasi hingga 87% serta memperbaiki keseimbangan distribusi prediksi antar bidang ilmu. Pendekatan cGAN-BERT ini diharapkan menjadi solusi untuk mengatasi masalah data tidak seimbang dan memiliki potensi besar untuk diterapkan pada sistem klasifikasi otomatis artikel ilmiah
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