Model Deteksi Berita Hoaks Bahasa Indonesia Menggunakan Multinomial Naïve Bayes dan AdaBoost Classifier
DOI:
https://doi.org/10.47065/bulletincsr.v6i2.927Keywords:
AdaBoost; Hoax News; CRISP-DM; Text Classification; TF-IDFAbstract
The rapid growth of the internet has led to the massive and uncontrolled dissemination of information across various digital platforms, allowing hoax news to reach a wide audience and influence public opinion in a short period of time. This condition highlights the need for a reliable automated detection system. However, existing methods still face limitations in terms of accuracy, result stability, and reliance on manual verification processes. Therefore, this study aims to compare and analyze the performance of two classification algorithms in detecting Indonesian-language hoax news accurately and effectively. This study follows the CRISP-DM framework, beginning with the collection of hoax and non-hoax news articles from turnbackhoax.id and detik.com, resulting in 2,281 samples. The data understanding stage involves analyzing dataset characteristics and evaluating data quality. During data preparation, text elements that explicitly indicate hoax labels are removed, followed by feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF). The dataset is then trained and tested using data split ratios of 70:30, 80:20, and 90:10 by applying Multinomial Naïve Bayes and AdaBoost Classifier algorithms. Model performance is evaluated using a confusion matrix. The results show that AdaBoost achieves superior performance, with an accuracy of 0.9879 (98.79%), outperforming Multinomial Naïve Bayes, which attains an accuracy of 0.9712 (97.12%). The performance of AdaBoost is also consistent across different evaluation scenarios, indicating that it is more suitable as an automated hoax news detection model for the dataset used in this study.
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