Implementasi Algoritma Support Vector Machine untuk Analisis Sentimen LGBT di Indonesia - Mustakim Mustakim; Muhammad Ridwan; Nanda Try Luchia (Graha Mitra Edukasi, 2022)
LGBT cases began to appear openly in Indonesia in 2016. This case has received a lot of discussion in that year until now because of the number of people who commented agreeing and disagreeing with actions, activities, and the existence of the LGBT gender in Indonesia. The sentiments from the community's comments refer to various aspects of life so as to produce community opinions that are positive, negative and neutral. Seeing this, it is necessary to perform a classification and analysis of tweet sentiments to see the tendency of each community's opinion. Analysis and classification is done with text mining data processing techniques using the Support Vector Machine (SVM) algorithm. The classification process is done in 3 stages with the division of data 90%:10%, 80%:20% and 70%:30% using 3 kernels namely linear, polynominal and Radial Basic Function (RBF). The classification results obtained from the three kernels show that the tendency of society's view of LGBT cases is negative and neutral which is shown with the highest accuracy on the linear and RBF kernels. The SVM experiment produced an accuracy of 74% on the linear kernel with 90%:10% and 74% data experiments and on the RBF kernel with C=100 gamma=0,01. The grouping of this tweet sentiment data resulted in an analysis of the tendency not to support or disagree with the LGBT gender because it is not in accordance with the established basis in the country of Indonesia which prioritizes religious aspects over other aspects