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Study compares AutoML and BiLSTM for Indonesian Instagram cyberbullying detection

This research paper compares automated machine learning (AutoML) and Bidirectional Long Short-Term Memory (BiLSTM) models for detecting cyberbullying in Indonesian Instagram comments. The study utilized a dataset of 650 comments, evaluating various machine learning algorithms like Logistic Regression and deep learning models including BiLSTM with Attention. Results indicated that Logistic Regression was the top performer among traditional machine learning methods, while BiLSTM with Attention showed the best deep learning performance, underscoring the importance of tailored preprocessing for informal Indonesian text. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Provides insights into effective NLP techniques for informal language, potentially aiding social media platforms in content moderation.

RANK_REASON Academic paper comparing machine learning and deep learning models for a specific NLP task.

Read on arXiv cs.CL →

COVERAGE [2]

  1. arXiv cs.CL TIER_1 · Raihana Adelia Putri, Aisyah Musfirah, Anggi Puspita Ningrum, Luluk Muthoharoh, Ardika Satria, Martin Clinton Tosima Manullang ·

    Comparative Analysis of AutoML and BiLSTM Models for Cyberbullying Detection on Indonesian Instagram Comments

    arXiv:2604.26229v1 Announce Type: new Abstract: This study compares machine learning and deep learning approaches for cyberbullying detection in Indonesian-language Instagram comments. Using a balanced dataset of 650 comments labeled as Bullying and Non-Bullying, the study evalua…

  2. arXiv cs.CL TIER_1 · Martin Clinton Tosima Manullang ·

    Comparative Analysis of AutoML and BiLSTM Models for Cyberbullying Detection on Indonesian Instagram Comments

    This study compares machine learning and deep learning approaches for cyberbullying detection in Indonesian-language Instagram comments. Using a balanced dataset of 650 comments labeled as Bullying and Non-Bullying, the study evaluates Naive Bayes, Logistic Regression, and Suppor…