Implementasi Support Vector Machine untuk Deteksi Stres pada Pengguna E-Learning

Penulis

Fajar Pradana, Fitra A. Bachtiar, Muhammad Zulfikarrahman

Abstrak

Pada masa ini, e-learning cenderung monoton yang hanya digunakan untuk otomasi pekerjaan saja. Pada pengembangan e-learning yang akan datang, e-learning menerapkan lingkungan adaptif agar hasil yang didapatkan dari penggunaan e-learning dapat menjadi lebih optimal. Salah satu strategi agar e-learning menjadi adaptif adalah adaptasi dengan kondisi mental pengguna. Contoh kasus ketika pengguna stres maka sistem e-learning yang adaptif akan memberikan materi latihan yang lebih mudah atau memberi notifikasi untuk istirahat. Deteksi stres dapat dilakukan dengan pengolahan data dari sinyal fisiologis, yaitu heart rate. Metode klasifikasi Support Vector Machine diterapkan untuk deteksi stres. Fitur yang digunakan untuk klasifikasi stres adalah fitur yang berasal dari domain Heart Rate Statistical. Pengujian akurasi metode Support Vector Machine terhadap kasus pengguna e-learning mampu menghasilkan akurasi sampai 58,3% dengan menggunakan 12 sampel data.

 

Abstract

This time, e-learning tends to be monotonous which is only for job automation. In future of e-learning development, e-learning will apply adaptive environment so that the result obtained from e-learning can be more optimal. One of the strategies to turn e-learning to be adaptive is adaptation to user’s mental condition. By example, when user is stressed then adaptive e-learning system will provide easier exercise or pop notification for break. Stress detection can be achieved by processing data from physiological signal that is heart rate. The Support Vector Machine classification method can be implemented for stress detection. The features that used for stress detection are derived features from Heart Rate Statistical domain. The Support Vector Machine validation testing on case of e-learning users able to provide 58,3% accuracy by using 12 samples of data.


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Referensi


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DOI: http://dx.doi.org/10.25126/jtiik.2021844371