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

Full Text:



A. Sangrà, D. Vlachopoulos, and N. Cabrera, “Building an inclusive definition of e ­ learning : An approach to the conceptual framework | Sangrà | The International Review of Research in Open an … Building an inclusive definition of e ­ learning : An approach to the conceptual framework,” Int. Rev. Res. Open Distrib. Learn., vol. 13, no. 2, pp. 145–159, 2012.

M. R. Elabnody, “A Survey Of Top 10 Open Source Learning Management Systems,” Int. J. Sci. Technol. Res., vol. 4, no. 8, pp. 7–11, 2015.

A. Büchner, Moodle 3 Administration, Third. PACKT Publishing, 2016.

Moodle, “Moodle,” 2020. [Online]. Available: [Accessed: 23-Oct-2020].

Moodle, “Moodle Statistics.” [Online]. Available: [Accessed: 23-Oct-2020].

Á. Herrero et al., “Preface,” in Advances in Intelligent Systems and Computing, vol. 239, 2014, pp. v–vi.

E. Młynarska, D. Greene, and P. Cunningham, “Time series clustering of Moodle activity data,” CEUR Workshop Proc., vol. 1751, pp. 104–115, 2016.

B. Liu, Web Data Mining : Exploring Hyperlinks, Contents, and Usage Data. Leipzig: Springer.

D. Rachmatin, “Aplikasi Metode-Metode Agglomerative Dalam Analisis Klaster Pada Data Tingkat Polusi Udara,” Infin. J., vol. 3, no. 2, p. 133, 2014, doi: 10.22460/infinity.v3i2.59.

R. Silvi, “Analisis Cluster dengan Data Outlier Menggunakan Centroid Linkage dan K-Means Clustering untuk Pengelompokkan Indikator HIV/AIDS di Indonesia,” J. Mat. “MANTIK,” vol. 4, no. 1, pp. 22–31, 2018, doi: 10.15642/mantik.2018.4.1.22-31.

A. Bovo, S. Sanchez, O. Heguy, and Y. Duthen, “Clustering moodle data as a tool for profiling students,” 2013 2nd Int. Conf. E-Learning E-Technologies Educ. ICEEE 2013, vol. 4, no. 3, pp. 121–126, 2013, doi: 10.1109/ICeLeTE.2013.6644359.

A. Aditya, I. Jovian, and B. N. Sari, “Implementasi K-Means Clustering Ujian Nasional Sekolah Menengah Pertama di Indonesia Tahun 2018/2019,” J. Media Inform. Budidarma, vol. 4, no. 1, p. 51, 2020, doi: 10.30865/mib.v4i1.1784.

C. Pradana, S. S. Kusumawardani, and A. E. Permanasari, “Comparison Clustering Performance Based on Moodle Log Mining,” in IOP Conference Series: Materials Science and Engineering, 2020, vol. 722, no. 1, doi: 10.1088/1757-899X/722/1/012012.

S. Misra, H. Li, and J. He, Machine Learning for Subsurface Characterization. Cambridge: Elsevier Inc, 2020.

P. Giordani, F. M. Brigida, and F. Martella, An Introduction to Clustering, 1st ed., vol. 1. Springers, 2020.

H. Singh and K. Kaur, “New Method for Finding Initial Cluster Centroids in K-means Algorithm,” Int. J. Comput. Appl., vol. 74, no. 6, pp. 27–30, 2013, doi: 10.5120/12890-9837.

M. J. Zaki and W. Meira, “Fundamental concepts and Algorithms,” 2014.

F. Gorunescu, Data Mining : Concepts, Models and Techniques. Verlag Berlin Heidelberg: Springers, 2011.


Article Metrics

Abstract view : 922 times
PDF - 434 times


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.