Implementasi Algoritma Random Forest untuk Analisis Sentimen Ulasan Pengguna Aplikasi Merdeka Mengajar
DOI:
https://doi.org/10.47065/bulletincsr.v5i4.530Keywords:
Sentiment Analysis; Merdeka Mengajar; Naive Bayes; SVM; Random ForestAbstract
Education plays a major role in determining the quality of human resources. The role of teachers is very important as educators who provide guidance and learning. As an effort to facilitate teachers to carry out their duties and responsibilities, especially in the Merdeka Mengajar curriculum, the Ministry of Education and Culture has developed an application called Merdeka Mengajar. However, there is no method to classify sentiment or opinions from comment data on the Merdeka Mengajar application user satisfaction survey on the Google Playstore, in order to determine the extent of user satisfaction with the Merdeka Mengajar application. This study aims to observe sentiment analysis regarding user opinions on the Merdeka Mengajar application on the Google Playstore using the Random Forest, SVM and Naïve Bayes algorithms using TF-IDF weighting for the classification process. This study uses secondary data derived from user reviews of the Merdeka Mengajar application and is classified using the Random Forest, SVM, and Naïve Bayes methods. The results of the classification show that the Random Forest algorithm is the best algorithm in predicting Merdeka Mengajar application user reviews compared to Naive Bayes and SVM.
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Copyright (c) 2025 Yuwan Jumaryadi, Ruci Meiyanti, Riri Fajriah, Athiyyah Nisrina Mahsyar, Puspita Sari Anggraeni
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