Educational data mining for mapping the comprehension of personality subject using K-Means algorithm (case study of SD Insan Mulya)
DOI:
https://doi.org/10.35335/mandiri.v11i4.194Keywords:
Clusterizations, Educational Data Mining, K-Means Algorithm, Mapping, Subject PersonalityAbstract
The government has emphasized the importance of character education since the elementary school (SD) level by providing various subjects as an effort to build personality/character for each student. It is important for teachers and schools to know the level of understanding in their students. This research is for. This study uses the K-Means Clustering Algorithm method to help map students to the level of understanding of the Personality subject. The results of this study indicate that clustering using the K-Means Algorithm method contains 4 effective clusters in mapping the understanding of personality subjects. The result of the percentage of clusterization results with interviews is 89.39% while the percentage of software is 91.70%. The conclusions obtained in this research are the subjects of Moral Education, Religious Education, Citizenship Education are included in the Subjects that can affect the student's personality.
References
Aldino, A. A., Darwis, D., Prastowo, A. T., & Sujana, C. (2021). Implementation of K-Means Algorithm for Clustering Corn Planting Feasibility Area in South Lampung Regency. Journal of Physics: Conference Series, 1751(1), 012038. https://doi.org/10.1088/1742-6596/1751/1/012038
Dewi, S., Defit, S., & Yunus, Y. (2020). Akurasi Pemetaan Kelompok Belajar Siswa Menuju Prestasi Menggunakan Metode K-Means (Studi Kasus SMP Pembangunan Laboratorium UNP). Jurnal Sistim Informasi Dan Teknologi, 3, 7–10. https://doi.org/10.37034/jsisfotek.v3i1.98
Hidayat, M. M., Purwitasari, D., & Ginardi, H. (2013). Analisis Prediksi Drop Out Berdasarkan Perilaku Sosial Mahasiswa Dalam Educational Data Mining Menggunakan Jaringan Syaraf Tiruan. J. IPTEK, 17(2), 109–119.
Hussain, M., Liu, S., Ashraf, U., Ali, M., Hussain, W., Ali, N., & Anees, A. (2022). Application of Machine Learning for Lithofacies Prediction and Cluster Analysis Approach to Identify Rock Type. Energies, 15(12), Article 12. https://doi.org/10.3390/en15124501
Janusz, A., Jamiołkowski, A., & Okulewicz, M. (2022). Predicting the Costs of Forwarding Contracts: Analysis of Data Mining Competition Results. 2022 17th Conference on Computer Science and Intelligence Systems (FedCSIS), 399–402. https://doi.org/10.15439/2022F303
Khan, A., & Ghosh, S. K. (2021). Student performance analysis and prediction in classroom learning: A review of educational data mining studies. Education and Information Technologies, 26(1), 205–240. https://doi.org/10.1007/s10639-020-10230-3
Lemay, D. J., Baek, C., & Doleck, T. (2021). Comparison of learning analytics and educational data mining: A topic modeling approach. Computers and Education: Artificial Intelligence, 2, 100016. https://doi.org/10.1016/j.caeai.2021.100016
Tomaskova, H., & Tirkolaee, E. B. (2021). Using a Process Approach to Pandemic Planning: A Case Study. Applied Sciences, 11(9), Article 9. https://doi.org/10.3390/app11094121
Ulfah, M., & Irtawty, A. S. (2023). Implementation of Data Mining Clustering Using the K-Means Method in Grouping Library Books. International ABEC, 188–194.
Xiao, W., Ji, P., & Hu, J. (2022). A survey on educational data mining methods used for predicting students’ performance. Engineering Reports, 4(5), e12482. https://doi.org/10.1002/eng2.12482
Yağcı, M. (2022). Educational data mining: Prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments, 9(1), 11. https://doi.org/10.1186/s40561-022-00192-z
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Copyright (c) 2023 Wiwiet Herulambang, M Mahaputra Hidayat, Nabila Amanda Putri
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