PENERAPAN SUPPORT VECTOR MACHINE UNTUK ANALISIS SENTIMEN PADA TANGGAPAN MASYARAKAT DI MEDIA SOSIAL TERHADAP PROGRAM MAKAN SIANG GRATIS
APPLICATION OF SUPPORT VECTOR MACHINE FOR SENTIMENT ANALYSIS ON PUBLIC RESPONSE TOWARDS FREE LUNCH PROGRAM
DOI:
https://doi.org/10.36341/rabit.v10i2.6425Keywords:
sentiment analysis, Support Vector Machine, free lunch program, social media, TF-IDFAbstract
The Free Nutritious Lunch Program initiated by the government has become a public spotlight, as it is considered a strategic effort to address the issues of malnutrition and stunting in Indonesia. This study aims to analyze public sentiment toward the program using a technology-based approach. The method used is the Support Vector Machine (SVM) algorithm with the Term Frequency-Inverse Document Frequency (TF-IDF) approach to classify public opinions on social media. This study utilized 1,000 tweets collected from the Twitter platform and processed using Google Colaboratory. SVM was chosen because it can handle high-dimensional data and has proven effective for text classification in various previous studies. The analysis results show that the majority of public sentiment is positive. The SVM model achieved an accuracy of 67%, with a precision of 98%, recall of 99%, and F1-score of 98%, demonstrating its effectiveness in classifying textual data. These findings indicate that sentiment analysis using machine learning approaches can serve as an important tool in evaluating public perception of government policies.
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