<![CDATA[KIP-Kuliah program or The Indonesia Smart College Card (KIP-K) is a funding assistance from the government for students with economic difficulties who want to experience educational opportunities in higher education. This program can’t be separated from public discussion, especially regarding the issue of misuse of funds by recipients, inconsistency in fund disbursement and falsification of registration files. These problems make the public view that the KIP-K program is often still misdirected. The research aims to examine public sentiment or perception towards the KIP-K program using Random Forest algorithm combined with Word2Vec as a word weighting technique and Random Oversampling (ROS) as a balancing technique to overcome data imbalance. The dataset obtained comes via platform X or Twitter) a total of 4423 tweets with the keywords “kip-k” or “kipk” and with a vulnerable time during 2024. The model’s performance demonstrated a high accuracy of 96,57%, precision, recall, and f1-score at the same value of 97%. The results indicate that the model is effective in analyzing sentiment accurately and maintaining a balanced performance between the two sentiment classes. Based on research in this study, the Random Forest algorithm combined with Word2Vec and Random Oversampling (ROS) can produce high accuracy and can overcome data imbalance.]]>
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