PENGELOMPOKAN DATA NILAI SISWA MADRASAH TA’HILIYAH MENGGUNAKAN METODE K-MEANS CLUSTERING
DOI:
https://doi.org/10.69714/0v1pkz05Keywords:
Clustering, Data Mining, K-Means, PengelompokanAbstract
Data mining, or data mining is the process of collecting and processing data to extract important information. The stages in the data mining process are useful for finding a particular pattern from a large amount of assessment data. This goal is to find out and form student data clusters based on grades so that they become a cluster, so that the results of student clusters can be a reference in improving student grades in the next learning process. The results of the evaluation and assessment of students are carried out by teaching staff or teachers in conducting assessments during the learning process. In the learning process there are 2 assessment categories, namely UTS and UAS student grades. The results of grouping student grade data using the K-Means clustering method show that based on the results of student data clusters in one semester, cluster 0 is obtained with 7 students, cluster 1 is 3. The results of testing using rapid miner show that there are 7 students who have grades with a good average and there are 3 students with a poor average grade.
References
Manning, Raghavan, & Schütze, (2008).dengan buku Introduction to Information Retrieval. Cambridge University Press J.E. Bourne. “Synthetic structure of industrial plastics,” in Plastics, 2nd ed., vol. 3. J. Peters, Ed. New York: McGraw-Hill, 1964, pp.15-67.
moh. Syahrul Iskandar, Zaehol Fatah, “Implementasi Metode Algoritma K-Means Clustering Untuk Menentukan Penerima Program Indonesia Pintar (PIP)” Volume 2; Nomor 10; November 2024; Page 1-8
Tan, Steinbach, & Kumar, (2006). Introduction to Data Mining. Pearson Addison-Wesley.
Jain, (2010). Data Clustering: 50 Years Beyond K-Means. Pattern Recognition Letters, 31(8), 651-666.
Han, Kamber, & Pei. (2011). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann.
Sudjana, (2010). Statistik Deskriptif. Bandung: Tarsito..
Everitt, (2011). Cluster Analysis. 5th Edition. Chichester: Wiley.
Han, Kamber, & Pei. (2011). Data Mining: Concepts and Techniques (3rd Edition). San Francisco: Morgan Kaufmann Publishers.
Alpaydin. (2014). Introduction to Machine Learning (3rd Edition). Cambridge, MA: MIT Press.
Ng, A. (2018). Machine Learning Yearning. Stanford: Self-published.
Pang-Ning Tan, Michael Steinbach, & Vipin Kumar (2006). Introduction to Data Mining. Pearson.
"Pattern Recognition and Machine Learning" oleh Christopher Bishop.
"Data Mining: Concepts and Techniques" oleh Jiawei Han, Micheline Kamber, dan Jian Pei.
