SENTIMENT ANALYSIS OF THE LEADERSHIP OF THE PRESIDENT OF THE REPUBLIC OF INDONESIA USING THE SUPPORT VECTOR MACHINE METHOD

I Kadek Adi Kusuma Putra; Ida Bagus Gede Sarasvananda; Putu Shinta Noviaty; I Putu Agus Eka Darma  Udayana; Ketut Jaya Atmaja

Authors

I Kadek Adi Kusuma Putra Institut Bisnis dan Teknologi Indonesia
Ida Bagus Gede Sarasvananda Institut Bisnis dan Teknologi Indonesia
Putu Shinta Noviaty Institut Bisnis dan Teknologi Indonesia
I Putu Agus Eka Darma Udayana Institut Bisnis dan Teknologi Indonesia
Ketut Jaya Atmaja Institut Bisnis dan Teknologi Indonesia

Keywords:

Analysis Sentiment, Support Vector Machine, President of the Republic of Indonesia, Leadership Sentiment

Abstract

Sentiment analysis has become one of the most widely used methods to understand public opinion on various issues, including the leadership of the President of the Republic of Indonesia. The data for this study were obtained from YouTube and X platforms through data crawling techniques using Python, with the keyword #Jokowi. The methodology used in this study applies the Support Vector Machine (SVM) to categorize public views into positive and negative sentiment categories. The evaluation of the entire process in the program demonstrates the results of each step in the Support Vector Machine method. This evaluation uses metrics such as accuracy, precision, and recall. The results of the analysis obtained from the Support Vector Machine are visualized using a confusion matrix, yielding the following outcomes: True Negative 1,451, True Positive 2,600, False Positive 511, and False Negative 350. These results indicate an accuracy of 82%, precision of 83%, and recall of 88%. The confusion matrix results show that the model accurately identified 2,600 data points with an accuracy of 82%, precision of 83%, recall of 88%, and an F1-Score of 85%. Overall, this analysis demonstrates that the model performs better in recognizing positive sentiments compared to negative sentiments.

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