Perbandingan Metode Random Forest Classifier dan SVM Pada Klasifikasi Kemampuan Level Beradaptasi Pembelajaran Jarak Jauh Siswa - Ilham Adriansyah; Muhammad Diemas Mahendra; Errissya Rasywir; Yovi Pratama (Bulletin of Informatics and Data Science, 2022)
WHO has declared that COVID-19 or SARS-CoV-2 has been a global pandemic since March 2020. Distance learning as we often hear is learning that prioritizes independence. Teachers can deliver teaching materials to students without having to meet face to face in the same room. This kind of learning can be done at the same time or at different times. This study aims to compare the results of the classification of students' distance learning adaptability levels with the random forest classifier and SVM methods. Obtaining the evaluation results of each algorithm used. Precision, recall, f1-score, and accuracy are evaluation indicators. The results of the classification of each adaptivity class got 73.1% for Moderate, 74.7% for Low and 66.1% for High. With the total accuracy of the SVM algorithm on the tested data of 73.36%. The results of the classification of each adaptivity class got 92.1% for Moderate, 92% for Low and 86% for High. With the total accuracy of the Random Forest Classifier algorithm on the tested data, it is 91.5%. From 1205 test data contents for each model, it was found that the Random Forest model has a higher accuracy but has an incorrect classification value of 321 data, and the accuracy of the Support Vector Machine model is lower but has an incorrect classification value of as much as 101 data