Klasifikasi Teks Clickbait dengan Metode Deep Learning Hybrid LSTM-CNN - Darma, Aditya; Sepni, Sepni; Sary, Shelly Adelia; Hasanah, Yayang Uswatun (Universitas Muhammadiyah Riau, 2026)
This study aims to determine the category of news titles by dividing them into two groups, namely clickbait and non-clickbait using the LSTM-CNN hybrid method. The dataset used consists of 14,878 data in two categories with 6,285 clickbait news data and 8,693 non clickbait news data obtained from the kaggle page. The research stages include data preprocessing through cleaning, tokenizing, stopword removal, stemming, and text representation using the Word2Vec algorithm. The dataset will then be separated into training and test data using a ratio of 80:20. The LSTM-CNN hybrid model is used because of CNN's advantage in extracting local features as well as LSTM's ability to understand sequential relationships between words. The model performance evaluation was conducted using confusion matrix, with the results of 77.07% accuracy, 70% recall, 73% precision, and 71% F1-score. The LSTM-CNN hybrid model showed better performance than the separate models with an increase in accuracy from 77% to 77.07%. This research shows that the LSTM-CNN model combination is effective in handling clickbait and non-clickbait news text classification, providing quite good results in improving the performance of the previous model.