Analisis Sentimen Isu Childfree Di Media Sosial Twitter Menggunakan Algoritma Support Vector Machine - Nurhidayati, Lidya; Umaidah, Yuyun; Enri, Ultach (Jurnal Ilmiah Wahana Pendidikan, 2024)
AbstractSentiment analysis is a process to process, convert and interpret a text and classify it in the form of positive and negative sentiments. The phenomenon of childfree in Indonesia is currently causing debate and has become a trending topic on several social media, especially Twitter. The assumption that childfree decisions are categorized as selfish decisions is certainly closely related to the patriarchal culture that exists in Indonesia. This patriarchal culture is certainly very much in line with the concept of gender construction, where the childfree decision for women is considered a form of female selfishness. Based on this, an analysis of public sentiment related to the issue of childfree on Twitter social media using the Support Vector Machine (SVM) algorithm using 4 kernels. This research uses the KDD method by going through the stages of data selection, preprocessing, transformation, data mining, and evaluation. The data used are tweets totaling 1,447 tweets. The data was then selected into 1,447 which were divided into 1178 positive label data and 226 negative label data. In the data mining stage, the data is divided into 4 scenarios, namely 90:10, 80:20, 70:30, and 60:40. The best results were found in the first scenario with the Linear kernel, resulting in 75.93% accuracy, 83.33% precision, and 68.97% recall, showing the effectiveness of the algorithm in analyzing sentiment regarding the childfree phenomenon on Twitter. Keywords: Sentiment Analysis, Childfree, Support Vector Machine