<![CDATA[To prepare students to face social, cultural changes, the world of work and rapid technological advances, student competencies must be prepared to be more in line with the needs of the times. The Merdeka Belajar Kampus Merdeka policy is expected to be the answer to these demands. Since it was first launched in 2020, this curriculum has been implemented with several challenges such as four-year education planning, converting online course grades, converting internship credits, building partnerships, and rebuilding academic and administrative systems. The challenges experienced during the implementation of the MBKM program were almost entirely related to administration or paperwork and a few technical obstacles in the process. To find out the level of satisfaction of the public with government policy, sentiment analysis is needed on user opinions regarding the implementation of the program. This sentiment analysis can group the polarity of the text in a sentence or document to find out whether the opinion in the sentence or document is positive or negative. The method used is Support Vector Machine (SVM). Acquisition of comments from X platform produces 500 comments which are then divided into 400 training data and 100 test data. The weighting method used is tf-idf and testing is carried out using SVM with linear kernel and rbf. From the test results, it was obtained that SVM with a linear kernel with a cost parameter (c) of 1 produced the highest accuracy, namely 84%. It is hoped that this research can be used as material for consideration to improve the implementation process of independent campus learning programs.]]>
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