Improving Multi-label Classification Performance on Imbalanced Datasets Through SMOTE Technique and Data Augmentation Using IndoBERT Model

Improving Multi-label Classification Performance on Imbalanced Datasets Through SMOTE Technique and Data Augmentation Using IndoBERT Model

Sentiment and emotion analysis is a common classification task aimed at enhancing the benefit and comfort of consumers of a product. However, the data obtained often lacks balance between each class or aspect to be analyzed, commonly known as an imbalanced dataset. Imbalanced datasets are frequently...

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Bibliographic Details
Main Authors: Leno Dwi Cahya, Ardytha Luthfiarta, Julius Immanuel Theo Krisna, Sri Winarno, Adhitya Nugraha
Format: Article
Language:Indonesian
Published: Universitas Andalas 2024-01-01
Series:Jurnal Teknologi dan Sistem Informasi
Subjects:
imbalanced
smote
augmentation
sentiment
nlp
Online Access:https://teknosi.fti.unand.ac.id/index.php/teknosi/article/view/2503

Internet

https://teknosi.fti.unand.ac.id/index.php/teknosi/article/view/2503

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