EFFECTIVENESS OF DEEP LEARNING APPROACH FOR TEXT CLASSIFICATION IN ADAPTIVE LEARNING
DOI:
https://doi.org/10.21107/kursor.v11i3.285Keywords:
Adaptive learning, text classification, CNN, RNN, HAN, Word2VecAbstract
Klasifikasi text sangat bermanfaat dan dibutuhkan diberbagai bidang. Salah satu bidang yang membutuhkan klasifikasi text adalah E-learning yang bersifat adaptive atau disebut sebagai adaptive learning sistem. Adaptive learning sytem adalah sistem pembelajaran online yang dapat memberikan rekomendasi pembelajaran berdasarkan kebutuhan pengguna. Adaptive learning memiliki dua bagian, yaitu modul learning dan modul testing. Modul learning adalah bagian dari sistem yang bertugas untuk memberikan rekomendasi pembelajaran bagi pengguna, sedangkan modul testing bertugas untuk menguji dan memberikan penilaian terhadap hasil pembelajaran yang diperoleh dari modul learning. Materi pembelajaran pada modul learning memerlukan klasifikasi text berdasarkan tingkat kesulitannya untuk memastikan bahwa pengguna dengan level kemampuan rendah juga mendapatkan materi pembelajaran yang mudah, dan rekomendasi ini akan dinamis mengikuti perkembangan kemampuan pengguna. Pada penelitian ini, akan dibahas bagian kecil dari sistem pembelajaran adaptive pada modul learning yaitu tahap klasifikasi text. Dataset yang digunakan dalam penelitian ini adalah mata pelajaran IPA untuk tingkat SMP yang didapatkan dari Ruang Guru dan merupakan salah satu platform E-learning di Indonesia. Metode yang digunakan dalam penelitian ini adalah CNN, RNN dan HAN dengan menggunakan word embedding Word2Vec
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