BERT SENTIMENT ANALYSIS FOR DETECTING FRAUDULENT MESSAGES

Abstract

With the increasing prevalence of digital communication, fraudulent SMS messages have become a growing concern. This study employs a BERT-based sentiment approach to classify SMS messages into four categories: fraud, gambling, Unsecured Credit (KTA – Kredit Tanpa Agunan), and others. These categories were determined based on content analysis and common patterns found in high-risk messages, such as suspicious transaction invitations (fraud), betting promotions (gambling), offers for unsecured loans (KTA), and other messages that do not fall into the three main categories. The dataset used consists of approximately 20,000 message records, which underwent data cleaning, tokenization, and manual labeling based on the aforementioned criteria. The model was trained using the AdamW optimizer with CrossEntropyLoss as the loss function for multi-class classification. Training was conducted over 3 epochs, a number chosen based on observations of evaluation metrics on the validation data, which showed that model accuracy began to plateau after the third epoch, while overfitting started to occur in subsequent epochs. After training, the model achieved an average accuracy of 92%. This result indicates that the BERT model is effective in understanding patterns in text messages and capable of classifying message categories with a high level of accuracy. These findings support the application of BERT technology in the efficient detection and identification of fraudulent messages.