Classification Analysis of Student Ability in Learning Using Clustering Method at SMA Tunas Pelita
Keywords:data mining, k-means, class X average value
This study aims to classify the assessment of the learning process at SMA Tunas Pelita Binjai T.A. 2018/2019 based on the average grade X, additional subjects applied technology, and student absenteeism classified using Matlab.The data is processed based on learning grouping as much as 2 clusters with different centroids, namely for cluster 1 the average value of even and odd semesters for class X (85.0), additional subjects of applied technology (86.3) and student attendance (2.4) and cluster 2 the average grades of odd-even semesters for class X (68.2), additional subjects of applied technology (70.3) and student attendance (2.4). In the final result, it can be seen that the grouping of learning at SMA Tunas Pelita Binjai with 100 data can be divided into 2 groups, namely group 1 with 62 data with an average value of odd and even semesters and high additional applied technology and student absenteeism. low grades are classified as students with good grades and group 2 as many as 38 data with an average value of odd, even semesters and low values of applied technology and high student absenteeism belonging to students who have poor grades.
Asriningtias, Y and Rodhiyah Mardhiyah.2014. Data Minung Aplication to display Information Student Graduation Rate. media.neliti.com. Jurnal Informatika Vol.8, No.1 Januari 2014
Azevedo, A. and Santos, M. F. KDD, SEMMA and CRISP-DM: a parallel overviewArchived 2013-01-09 at the Wayback Ma-chine. In Proceedings of the IADIS European Conference on Data Mining 2008, pp 182–185.
Dudita, Windha Mega Pradnya. 2015. Using the K-Means Clustering to Determine the Nutritional Status of Toddlers. Jurnal Informatika, Vol. 15, No. 2, Desember 2015.
Fayyad, Usama; Piatetsky-Shapiro, Gregory; Smyth, Padhraic (1996). "From Data Mining to Knowledge Discovery in Data-bases" (PDF). Retrieved 17 December 2008.
Han, Jiawei; Kamber, Micheline (2001). Data mining: concepts and techniques. Morgan Kaufmann. p. 5. ISBN 978-1-55860-489-6. Thus, data mining should have been more appropriately named "knowledge mining from data," which is unfortunately somewhat long
Piatetsky-Shapiro, Gregory; Parker, Gary (2011). "Lesson: Data Mining, and Knowledge Discovery: An Introduc-tion". Introduction to Data Mining. KD Nuggets. Retrieved 30 August 2012.
Pratama, Anindito Yoga and Yuhilza Hanum. 2016. Implementasi of Data Mining Techniques to Determine the Results of SMAN 99 Jakarta entry Selection for Students of SMPN 9 Jakarta Using Decision Tree. ejournal.gunadarma.ac.id. Vol 21. No. 1 2016
Sibuea, Fitri Larasati and Andi Sapta. 2016. Mapping of Outstanding Students Using the K-Means Clustering Method. Jurnal.stmikroyal.ac.id. VolIV No.1 Desember 2017. hlm.85-92.
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