Klasifikasi Hoaks Kesehatan di Media Sosial menggunakan Support Vector Machine - Aulia Rahma Hidayat; Putra Pandu Adikara; Sigit Adinugroho (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2020)
Of the various types of communication tools available, social media is often used by the people of Indonesia, but as a communication tool that is often used, not everything that is found in social media is true. As part of the communication tool used by everyone it is not uncommon to find unclear sources or Hoaks. Hoaks about health are widely spread on Social Media and this can affect public awareness of the importance of health. Separating true and untrue health news needs to be done to avoid this. The separation process is carried out by classifying health news on Social Media with the Support Vector Machine method with Bag of Words and Lexicon Based Features. Total data in this study were 80 news from various social media. The data is then entered in the pre-processing process to get the word that shows a document, then proceed to the word weighting process using the TF-IDF calculation. The results of the word weighting process are included in the core process, namely the calculation of the Support Vector Machine method. Optimal parameter test results obtained gamma value (Î³) = 0.001, lambda value (Î») = 1, epsilon value = 0.000001, degree value (d) = 2 and the maximum iteration value = 30. The results of system evaluation using both features get results which is good compared to using just one feature, showing the results of Accuracy of 1; Precision of 1; Recall of 1; F-measure of 1. Testing using K-fold Cross Validation was also carried out with a fold value of 10 and obtained an average value of Accuracy results of 0.6; Precision of 0.68; Recall of 0.47; F-measure of 0.48.