Sentiment Analysis of the Merdeka Campus Curriculum Based on Comments on the X Platform Using Naive Bayes and Random Forest Methods
Analisis Sentimen Terhadap Kurikulum Kampus Merdeka dari Komentar di Platform X Menggunakan Metode Naive Bayes dan Random Forest
DOI:
https://doi.org/10.21070/ups.12421Keywords:
MBKM, MBKM Program, Merdeka CampusAbstract
Merdeka Belajar Kampus Merdeka (MBKM) Program is a government policy designed to improve higher education through flexible, work-oriented learning. Its implementation has generated diverse public responses on social media, particularly X. This study analyzes public sentiment toward the Merdeka Campus Curriculum using Naive Bayes and Random Forest. Data were collected through Selenium-based web scraping in several sessions until mid-2026 and balanced using random undersampling, resulting in 1,051 tweets comprising 351 Neutral, 350 Positive, and 350 Negative tweets. The research involved preprocessing, TF-IDF feature extraction, an 80:20 train-test split, and classification. Random Forest achieved 63.64% accuracy, 65.14% precision, 63.66% recall, and 62.55% F1-score, while Naive Bayes achieved 55.50%, 54.35%, 55.43%, and 53.93%, respectively. Thus, Random Forest showed better performance than Naive Bayes in classifying MBKM-related sentiment on X.
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