Public Opinion Sentiment Analysis of Government Fuel Purchasing Policy by the Private Sector Using Support Vector Machine (SVM) Methods

Abstract

Government policies that provide opportunities for the private sector to participate in the purchasing and distribution of fuel oil (BBM) have triggered various reactions within society. The diversity of opinions expressed on social media reflects public perceptions of the effectiveness and potential impacts of these policies. This study aims to examine public sentiment toward the government policy by applying the Support Vector Machine (SVM) method. Data were collected from various social media platforms containing public responses to the issue of private sector involvement in fuel purchasing. The analysis process consisted of several stages, including data collection, data preprocessing (comprising cleansing, tokenizing, stopword removal, and stemming), feature extraction using the Term Frequency Inverse Document Frequency (TF-IDF) approach, and sentiment classification using the SVM algorithm. The results show that the SVM algorithm performs well in classifying public opinions into two sentiment categories, positive and negative, with a relatively high level of accuracy. The analysis indicates that the majority of public opinions tend to be negative, driven by concerns over potential price disparities, weakened government oversight, and possible socio-economic impacts. The findings of this study are expected to provide constructive input for the government in evaluating and developing energy policies that are more transparent and oriented toward public interest.