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All Journal VISA: Journal of Vision and Ideas
Hasanuddin, Ardy Satria
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Economics, Econometrics & Finance Environmental Science Languange, Linguistic, Communication & Media Public Health Social Sciences

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Klasifikasi Berita Negatif Menggunakan Bidirectional LSTM pada Dataset Berita Berbahasa Inggris Hasanuddin, Ardy Satria; Ariessanti, Hani Dewi
VISA: Journal of Vision and Ideas Vol. 5 No. 2 (2025): Journal of Vision and Ideas (VISA)
Publisher : IAI Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/visa.v5i2.9305

Abstract

The increasing consumption of digital news by Generation Z carries the risk of exposure to negative content, which can adversely affect mental health. According to the Stress in America™ report by the APA (American Psychological Association) in 2018, there are five main categories that cause the most stress among Gen Z: mass shootings, suicide, climate change, deportation of immigrants, and sexual harassment or assault. This study developed a negative news classification model using the Bidirectional Long Short-Term Memory (Bi-LSTM) algorithm. The research was conducted through several stages: data collection from the Mata.Today platform (which provides news summaries from various trusted sources), text preprocessing, automatic labeling based on APA’s psychological criteria, use of GloVe embeddings, Bi-LSTM model training, and evaluation using accuracy, precision, recall, and F1-score metrics. The implemented model, utilizing pre-trained GloVe embeddings, achieved an accuracy of 89.25% with an ROC AUC of 0.9528 on a test set of 1,200 data points, demonstrating the model’s ability to distinguish negative news (negative class recall = 89.98%) and non-negative news (recall = 88.33%).  
Co-Authors Hani Dewi Ariessanti