Adaptive E-Learning System Berbasis Vark Learning Style dengan Klasifikasi Materi Pembelajaran Menggunakan K-NN (K-Nearest Neighbor)

Akmal Agung Nugraha; Utomo Budiyanto

doi:10.33050/tmj.v7i2.1900

Authors

Akmal Agung Nugraha Magister Ilmu Komputer Universitas Budi Luhur, DKI Jakarta
Utomo Budiyanto Universitas Budi Luhur

DOI:

https://doi.org/10.33050/tmj.v7i2.1900

Abstract

This research is about Adaptive E-Learning System Based on VARK (Visual, Aural, Read/Write & Kinesthetic) Learning Style With Classification of Learning Materials Using K-NN (K-Nearest Neighbor). The world of education today must follow technological developments, one of which is by utilizing learning using e-learning, one of the shortcomings in e-learning that currently exists is that most provide the same material to all students, in fact every student has a different learning style. different in absorbing learning material. This Adaptive E-Learning System adopts VARK Learning Style in classifying student learning styles into four classes (Visual, Aural, Read/Write & Kinesthetic). At the beginning of using e-learning students are required to fill out a questionnaire based on the VARK instrument and will be assigned to one of the four classes according to their learning style tendencies. Students will get material according to their class with the K-NN (K-Nearest Neighbor) classification method. In this study, the classification of learning materials used 60 learning materials as datasets with visual, aural, read/write & kinesthetic labels, with 48 training data and 12 testing data divided into 91% accuracy, 93% precision and 91% recall.

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