Prediksi Kinerja Siswa Pada E-Learning Moodle Platform Menggunakan Algoritma Adaptive Boosting

Jordy Lasmana Putra; Agus Subekti

doi:10.31294/inf.v10i1.15525

Prediksi Kinerja Siswa Pada E-Learning Moodle Platform Menggunakan Algoritma Adaptive Boosting

Jordy Lasmana Putra, Agus Subekti

Abstract

Pandemi Covid-19 yang sudah berlangsung sejak awal tahun 2020 memberikan dampak besar di berbagai sektor, salah satunya di sektor pendidikan, dimana awalnya pendidikan dilakukan secara tatap muka, karena pandemi mengharuskan proses belajar mengajar dilakukan secara dalam jaringan (daring) Teknologi informasi berkembang sangat pesat dan mempengaruhi berbagai bidang, salah satunya bidang pendidikan, yang dimana pembelajaran secara daring sudah menjadi hal yang biasa untuk era saat sekarang ini, salah satu Learning Management System atau yang sering disingkat LMS yang sering digunakan adalah E-Learning menggunakan platform moodle, ditambah untuk saat pandemic covid-19 proses pembelajaran diarahkan ke sistem daring, sehingga penggunaan E-Learning menjadi meningkat. Melihat hal tersebut penulis bermaksud untuk melakukan penelitian untuk melakukan prediksi terhadap kinerja siswa dalam mengikuti perkuliahan e-learning yang menggunakan moodle platform, penelitian ini melihat dari sisi log activity siswa di moodle platform lalu log tersebut di transformasi agar dapat dilakukan proses klasifikasi oleh algoritma machine learning. Pada penelitian ini penulis melakukan klasifikasi menggunakan algoritma Adaptive Boosting dengan Base Learner C4.5 dengan teknik pra pemrosesan data Resample untuk Imbalance data. Hasil dari penelitian ini didapatkan hasil performansi yang baik, dengan nilai Akurasi 95%, ROC 0.97, dan Kappa 0.90. sehingga penelitian ini dapat menjadi model untuk memprediksi kinerja siswa dengan melihat log aktivitasnya menggunakan platform moodle.

The Covid-19 pandemic, which has been going on since the beginning of 2020, has had a major impact in various sectors, one of which is in the education sector, where initially education was carried out face-to-face, because the pandemic requires the teaching and learning process to be carried out online Information technology is developing very rapidly and affecting various fields, one of which is the field of education, where online learning has become commonplace for today's era, one of the Learning Management Systems or often abbreviated as LMS that is often used is E-Learning using the moodle platform, plus during the Covid-19 pandemic the learning process is directed to an online system, so that the use of E-Learning becomes increasing. Seeing this, the author intends to conduct research to predict student performance in participating in e-learning lectures using the moodle platform, this study looks at the student activity log on the moodle platform and then the log is transformed so that the classification process can be carried out by machine learning algorithms. In this study, the authors classified using the Adaptive Boosting algorithm with Base Learner C4.5 with the Resample data preprocessing technique for data imbalance. The results of this study obtained good performance results, with an Accuracy value of 95%, ROC 0.97, and Kappa 0.90. So this study can be a model to predict student performance by looking at their activity logs using the Moodle platform.

Keywords

Kinerja Siswa, E-Learning, Adaptive Boosting

Full Text:

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DOI: https://doi.org/10.31294/inf.v10i1.15525