Klasifikasi Tingkat Kemampuan Adaptasi Siswa dalam Pembelajaran Online Menggunakan Decision Tree

Advances in science and technology encourage adaptation to the utilization of technology in
various sectors, such as communication, education, and information. Especially in the context of
educational technology, it can be observed that online learning is gaining significant popularity
in various educational institutions. Therefore, it is important to explore how well learners can
adapt to the online learning environment. Predicting learners' adaptation level has great
significance for educators and developers of online learning platforms, with the aim of improving
the efficiency and quality of the learning experience. This research uses a dataset from Kaggle
by applying the Decision Tree Algorithm approach. The research results obtained an accuracy
of 95%, an increase of 7.44% from previous research which only obtained an accuracy of 87.56%
using the same algorithm without Feature engineering. This shows that Feature Engineer play
an important role in classifying students' adaptation levels to get good results with high accuracy.