ANALYSIS OF PUBLIC SENTIMENT TOWARDS THE TAPERA PROGRAM ON SOCIAL MEDIA X AND YOUTUBE USING SVM AND NAIVE BAYES - Suputra, I Made Egar; Iswara, Ida Bagus Ary Indra; Ratnaningrum, Luh Putu Rara Ayu; Kusuma, Aniek Suryanti; Yanti, Christina Purnama (Proceeding International Conference on Information Technology, Multimedia, Architecture, Design, and E-Business, 2024)
This research aims to determine public reactions to the government program, Public Housing Savings (Tapera), on Social Media X (formerly known as Twitter) and YouTube. The research employs machine learning techniques, specifically the Support Vector Machine (SVM) and Naive Bayes algorithms, to classify user sentiment into positive or negative categories. Data was collected during the period from May 20, 2024, to September 31, 2024, through the social media platforms X and YouTube, resulting in a total of 12337 data points, with 7511 data points processed for analysis. The classification results show that SVM achieved a higher accuracy score of 80.59%, compared to 52.23% for Naïve Bayes. Based on the research, it can be concluded that the Indonesian public provided more negative comments about TAPERA, reflecting dissatisfaction or concerns regarding the program's implementation. This research uses data from Social Media X and YouTube to understand public perceptions of this government program.