K-Medoids Algorithm Analysis in Grouping Students' Level of Understanding of Subjects


  • Ehrlich F.T Butarbutar STIKOM Tunas Bangsa Pematangsiantar, Sumatera Utara
  • M. Safii AMIK Tunas Bangsa Pematangsiantar, Sumatera Utara


Understanding of the material, Feedback, Data mining, Clustering, K-Medoids algorithm


Analysis of the teaching and learning process needs to be done as feedback on the understanding of the material for students. One of the obstacles faced by schools is that there is no method of how this feedback can be done so that student achievement is uneven. Student achievement in subjects can be seen from the results of the scores on the report cards obtained by students after taking the final semester exam. Due to the uneven achievement of students, it is necessary to make a method so that feedback analysis can be carried out on the level of student understanding of the subject. Is data mining with clustering techniques using the K-Medoids algorithm. With this algorithm, students' understanding of subjects with high potential can be grouped with high brightness average results


. Jo Shan Fu and J. S. Fu, “ICT in Education : A Critical Literature Review and Its Implications,” Int. J. Educ. Dev. Using Inf. Commun. Technol., vol. 9, no. 1, pp. 112–125, 2013.

S. Defiyanti, M. Jajuli, and N. Rohmawati, “K-Medoid Algorithm in Clustering Student Scholarship Applicants,” Sci. J. Informatics, vol. 4, no. 1, pp. 27–33, 2017, doi: 10.15294/sji.v4i1.8212.

Nurhayati, N. S. Sinatrya, L. K. Wardhani, and Busman, “Analysis of K-Means and K-Medoids’s Performance Using Big Data Technology,” 2018 6th Int. Conf. Cyber IT Serv. Manag. CITSM 2018, no. Citsm, pp. 1–5, 2019, doi: 10.1109/CITSM.2018.8674251.

D. F. Pramesti, Lahan, M. Tanzil Furqon, and C. Dewi, “Implementasi Metode K-Medoids Clustering Untuk Pengelompokan Data Potensi Kebakaran Hutan/Lahan Berdasarkan Persebaran Titik Panas(Hotspot),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 1, no. 9, pp. 723–732, 2017.

A. D. Andini and T. Arifin, “Implementasi Algoritma K-Medoids Untuk Klasterisasi Data Penyakit Pasien Di Rsud Kota Bandung,” J. RESPONSIF Ris. Sains …, vol. 2, no. 2, pp. 128–138, 2020.

A. Saxena et al., “A review of clustering techniques and developments,” Neurocomputing, vol. 267, pp. 664–681, 2017, doi: 10.1016/j.neucom.2017.06.053.

T. Bimantoro and A. K. Wardhani, “Implementasi Algoritma Partitioning Around Medoids Dalam Pengelompokan Restoran,” Indones. J. Technol. Informatics Sci., vol. 2, no. 1, pp. 33–36, 2020, doi: 10.24176/ijtis.v2i1.5651.

N. Hidayati, A. I. Rizmayanti, C. B. S. Dewi, R. Fatmasari, and W. Gata, “Penerapan Algoritma Klasterisasi dan Klasifikasi pada Tingkat Kepentingan Sistem Pembelajaran di Universitas Terbuka,” Swabumi, vol. 8, no. 2, pp. 134–142, 2020, doi: 10.31294/swabumi.v8i2.8385.

S. Liu and M. D’Aquin, “Unsupervised learning for understanding student achievement in a distance learning setting,” IEEE Glob. Eng. Educ. Conf. EDUCON, pp. 1373–1377, 2017, doi: 10.1109/EDUCON.2017.7943026.

B. Wira, A. E. Budianto, and A. S. Wiguna, “Implementasi Metode K-Medoids Clustering Untuk Mengetahui Pola Pemilihan Program Studi Mahasiwa Baru Tahun 2018 Di Universitas Kanjuruhan Malang,” RAINSTEK J. Terap. Sains Teknol., vol. 1, no. 3, pp. 53–68, 2019, doi: 10.21067/jtst.v1i3.3046.

A. M. H. Pardede et al., “Implementation of Data Mining to Classify the Consumer’s Complaints of Electricity Usage Based on Consumer’s Locations Using Clustering Method,” in Journal of Physics: Conference Series, 2019, vol. 1363, no. 1, doi: 10.1088/1742-6596/1363/1/012079.

W. Utomo, “The comparison of k-means and k-medoids algorithms for clustering the spread of the covid-19 outbreak in Indonesia,” Ilk. J. Ilm., vol. 13, no. 1, pp. 31–35, 2021, doi: 10.33096/ilkom.v13i1.763.31-35.

S. Sindi, W. R. O. Ningse, I. A. Sihombing, F. Ilmi R.H.Zer, and D. Hartama, “Analisis algoritma K-Medoids clustering dalam pengelompokan penyebaran Covid-19 di Indonesia,” Jti (Jurnal Teknol. Informasi), vol. 4, no. 1, pp. 166–173, 2020.

S. Samudi, S. Widodo, and H. Brawijaya, “The K-Medoids Clustering Method for Learning Applications during the COVID-19 Pandemic,” SinkrOn, vol. 5, no. 1, p. 116, 2020, doi: 10.33395/sinkron.v5i1.10649.

D. P. Sari and B. Budyanra, “The Risk Factor that Affect Children Diarrhea in The Island of Java 2013 (Riskesdas 2013 Data Analysis),” J. Educ. Heal. Community Psychol., vol. 6, no. 1, p. 1, 2017, doi: 10.12928/jehcp.v6i1.6615.

Syahrul dkk, “Teknologi informasi dan pendidikan,” Al-Manar (Edisi 1), vol. 12, no. 2, pp. 1–7, 2004.

E. H. S. Atmaja, “Implementation of k-Medoids Clustering Algorithm to Cluster Crime Patterns in Yogyakarta,” Int. J. Appl. Sci. Smart Technol., vol. 1, no. 1, pp. 33–44, 2019, doi: 10.24071/ijasst.v1i1.1859.

A. Hermawati, S. Jumini, M. Astuti, F. Ismail, and R. Rahim, “Unsupervised Data Mining with K-Medoids Method in Mapping Areas of Student and Teacher Ratio in Indonesia,” TEM J., vol. 9, no. 4, pp. 1614–1618, 2020, doi: 10.18421/TEM94-37.

A. Bhatti, J. Kim, and R. Li, “Implementation of Image Quality and Design Time for Block-based Lossy VQ Image Compression using K-Means and K-Medoids Algorithm in Spatial Domain,” Int. J. New Technol. Res., vol. 3, no. 12, p. 263166, 2017.




How to Cite

Butarbutar, E. F. ., & Safii, M. . (2021). K-Medoids Algorithm Analysis in Grouping Students’ Level of Understanding of Subjects. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 1(1), 27–34. Retrieved from https://ioinformatic.org/index.php/JAIEA/article/view/50