Penerapan K-Means Clustering dari Log Data Moodle untuk Menentukan Perilaku Peserta pada Pembelajaran Daring

Easbi Ikhsan



Pembelajaran berbasis daring atau e-learning mulai semakin banyak digunakan oleh para pengampu pelajaran melalui Learning Management System (LMS). Moodle sebagai LMS popular mulai banyak digunakan karena fiturnya yang lengkap. Selain itu moodle juga mampu merekam aktivitas pembelajaran para peserta melalui ketersediaan log data. Berbagai log data yang tersimpan dalam Moodle belum banyak dimanfaatkan oleh pengelola pendidikan ataupun para pengajar untuk mengevaluasi proses pendidikan dan pelatihan. Penelitian ini membahas mengenai analisis perilaku peserta pelatihan kursus Visualisasi Data dengan Tableau pada LMS moodle di situs Warung Kompetensi Pegawai Badan Pusat Statistik (Warkop BPS). Metode analisis yang digunakan ialah k-means clustering. Hasil penelitian menunjukkan bahwa perilaku peserta pelatihan dari kursus ini dapat dibagi menjadi 3 kelompok atau cluster berdasarkan aktivitas peserta pelatihan. Penelitian ini memberikan kesimpulan bahwa k-means clustering mampu memberikan informasi pengelompokan perilaku peserta kursus dari log data LMS sehingga kedepannya mampu melihat intervensi apa untuk meningkatkan semangat belajar para peserta pelatihan.

Kata kunci: Moodle, Log Data, Educational Data Mining


Online-based learning or e-learning is increasingly being used by instructors through the Learning Management System (LMS). Moodle as a popular LMS is starting to be widely used because of its complete features. In addition, Moodle is also able to record the learning activities of the participants through the availability of data logs. Various log data stored in Moodle have not been widely used by education managers or teachers to evaluate education and training progress. This study discusses the behavioral analysis of training participants in the data visualization course with Tableau on the Moodle LMS on the website of the Central Bureau of Statistics Employee Competency Stalls (Warkop BPS). The analytical methods used are Exploratory Data Analysis and k-means clustering. The results showed that the behavior of the trainees from this course could be divided into 3 groups or clusters based on the activities of the training participants. This study concludes that k-means clustering is able to provide information on the grouping of course participants' behavior from the LMS log data so that in the future they can see what interventions to increase the learning enthusiasm of the training participants.

Keywords: Moodle, Log Data, Educational Data Mining

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