Sentiment Analysis of YouTube Comments on the East Java Grant Case Using the Support Vector Machine (SVM) Algorithm

Analisis Sentimen Komentar Youtube Terhadap Kasus Dana Hibah Jawa Timur Menggunakan Algoritma Support Vector Machine (SVM)

Abstract

The phenomenon of corruption in Indonesia, particularly the alleged misuse of grant funds in East Java Province, has sparked various public responses expressed through comments on the YouTube platform. The dataset consists of 1,039 relevant comments collected from several YouTube videos related to the case. These comments underwent a series of text preprocessing stages, including case folding, tokenizing, text cleaning, stopwords removal, stemming, and transformation using Term Frequency-Inverse Document Frequency (TF-IDF). Subsequently, the data were manually labeled into three sentiment categories: positive, negative, and neutral. The dataset was then split into training and testing sets with an 80:20 ratio and used to train and evaluate the classification model. Model evaluation was performed using accuracy, precision, recall, and F1-score metrics. The results indicate that the SVM algorithm performs well in classifying public sentiment, making it a potential tool for automatically identifying public perceptions of emerging social and political issues.