Prediction and Detection of Scam Threats on Digital Platforms for Indonesian Users Using Machine Learning Models
DOI:
https://doi.org/10.51903/jtie.v3i3.208Keywords:
Scam Detection, Machine Learning, Digital SecurityAbstract
Scam threats on digital platforms continue to rise alongside the rapid adoption of technology in Indonesia. The unique characteristics of Indonesian digital users, such as low digital literacy and high social media usage, make them particularly vulnerable to various forms of scams, including phishing, impersonation, and emotional manipulation. This study aims to develop a machine learning-based model for predicting and detecting scams by identifying threat patterns within a local context. The methodology involves collecting a survey-based dataset from Indonesian digital users, capturing language patterns and user interaction behaviors. The dataset was processed through text-cleaning techniques, tokenization, normalization, and representation using TF-IDF and Word Embeddings. The machine learning models employed in this study are Random Forest and Support Vector Machine (SVM), evaluated using accuracy, precision, recall, and F1-score metrics. Hyperparameter tuning was conducted to optimize model performance, while k-fold cross-validation was utilized to minimize the risk of overfitting. The results indicate that the Random Forest model achieved the best performance, with an accuracy of 92.5%, precision of 90.7%, recall of 94.1%, and F1-score of 92.4%. The use of local datasets improved detection accuracy by 7.8% compared to global datasets, highlighting the critical importance of contextual representation in identifying scam patterns specific to Indonesia. The model was also effective in recognizing unique threat patterns, such as the use of informal language and manipulative phrases in scam messages. This study makes a significant contribution to the field of digital security by providing an effective machine learning-based approach to detecting scam threats in Indonesia. Moreover, the findings underscore the importance of developing local datasets and educating users as part of a holistic solution to enhance digital security. These insights emphasize the necessity of incorporating cultural and contextual factors into technology-driven approaches for combating scams in developing countries like Indonesia
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