Implementasi Algoritma SVM dalam Klasifikasi Komentar Judi Online menggunakan RapidMiner - Priti Rindi Artika; Khafifah Dwi Meilianasari; Ali Ikhwan (Jurnal Media Teknik Elektro dan Komputer, 2025)
The spread of negative comments containing elements of online gambling on digital platforms is increasingly affecting users. To address this issue, this study implemented a Support Vector Machine (SVM) algorithm to classify comments into two categories: those containing elements of online gambling and those without. The classification process was carried out using RapidMiner software, which allows data processing without the need for extensive coding. The dataset used was obtained from the Kaggle website and consisted of 8,442 comments. The data underwent preprocessing stages such as tokenization, normalization, and stopword removal. The SVM model was drilled and evaluated using cross-validation and evaluation metrics, with an accuracy of 97.91%, precision of 96.94%, recall of 99.81%, and an F1-score of 98.45%. The results showed that the SVM model achieved an accuracy of 97.91%, with high precision and recall across both classes. This demonstrates that the SVM algorithm is effective and efficient in automatically detecting comments containing elements of online gambling and is suitable for implementation as a content moderation system on digital platforms.