<![CDATA[Phishing messages on instant messaging applications such as WhatsApp pose a cybersecurity threat, particularly through social engineering techniques, making automated detection of Indonesian-language phishing messages important. This study aims to develop a phishing detection system for Indonesian-language WhatsApp messages using the Long Short-Term Memory (LSTM) model. The dataset consisted of 1,162 messages, comprising 817 non-phishing and 345 phishing messages, which were processed through cleaning, case folding, normalization, tokenization, stopword removal, stemming using Sastrawi, sequencing, and padding before model training. The LSTM model was trained using 5 epochs, a batch size of 32, a learning rate of 0.001, and the Adam optimizer, and evaluation using a confusion matrix achieved an accuracy of 88.0%, precision of 72.50%, recall of 96.67%, and F1-score of 82.86%. These results indicate that the model can support the initial identification of potentially phishing messages, particularly due to its high recall in detecting phishing messages, although the imbalanced class distribution remains a limitation that may affect classification performance.]]>
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