Analisis Sentimen Pergantian Kementerian Keuangan Indonesia dengan Menggunakan Algoritma Support Vector Machine
Abstract
The leadership transition within the Ministry of Finance of the Republic of Indonesia is a strategic event that has triggered various public responses. These reactions reflect the level of public trust in the government’s fiscal and economic policy directions. This study aims to analyze public sentiment regarding the leadership change using the Support Vector Machine (SVM) algorithm as the main classification method. The data were collected from public comments on YouTube videos discussing the Minister of Finance transition. The research stages included data collection, text preprocessing, sentiment labeling, feature extraction using the Term Frequency–Inverse Document Frequency (TF-IDF) method, and sentiment classification using SVM. Model evaluation was carried out through accuracy, precision, and recall metrics. The results show that the SVM algorithm achieved optimal classification performance with an accuracy of approximately 97%. The majority of the public expressed positive sentiment toward the leadership transition, while a smaller portion conveyed negative responses related to uncertainty about future economic policy directions.
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