Classification and Mapping of Online Gambling Based on News Articles Using NER and SVM
Abstract
The phenomenon of online gambling in Indonesia has developed rapidly, posing serious social and economic threats. This thesis aims to classify and map online gambling activities based on digital news using the Support Vector Machine (SVM) algorithm and Named Entity Recognition (NER). Data were collected from the news portals Detik.com, Kompas.com, and Tribunnews from 2017 to 2024 through a web scraping approach. The research process included setup and library import, data upload, data exploration, data labeling according to Law No. 1 of 2023, data preprocessing, data filtering, location normalization and extraction, and location data cleaning. Subsequently, the SVM model was trained for risk classification and followed by prediction. Evaluation was conducted using accuracy and F1-score metrics to assess overall model performance and classification balance. Based on the evaluation results, the Normal SVM model demonstrated the best performance with an accuracy of 96.94% and an F1-score of 0.97. The findings indicate that the combination of NER and SVM effectively identifies the location and risk level of online gambling activities. This research is expected to contribute to law enforcement authorities and policymakers in their efforts to prevent and address online gambling activities in Indonesia.
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