ANALISIS ALGORITMA K-MEANS CLUSTERING UNTUK PEMETAAN PRESTASI SISWA STUDI KASUS SMP NEGERI I SUKAHENING

ANALISIS ALGORITMA K-MEANS CLUSTERING UNTUK PEMETAAN PRESTASI SISWA STUDI KASUS SMP NEGERI I SUKAHENING

• By Warnilah, Ai Ilah;
• Jurnal Nasional Terakreditasi
• 340
• 83-95

The high success rate of students and the low level of student failure is a reflection of the quality of education. Education today are required to have the ability to compete by utilizing all available resources. In addition to resource infrastructure, facilities and people, information systems is one of the resources that can be used to improve the ability to compete. Data mining is the process of analyzing the data to find a pattern of the data set. Data mining is able to analyze a large amount of supporting data into information in the form that has meaning for decision support. One of the clustering process of data mining is one of the methods called k-means. K-Means algorithm is the simplest clustering algorithm than other clustering algorithms. Atribut group student achievement are the Name, extracurricular, value includes the value Skills Knowledge, Attitude value, and the number of absences students. From the results of a case study of 173 students obtained with manhattan distance, chbychep distance euclidian distance the result accuracy 67 %.

• Katakunci Clustering, K-means, student achievement
• URL https://ejournal.bsi.ac.id/ejurnal/index.php/ijcit/article/view/425
• Penerbit IJCIT (Indonesian Journal on Computer and Information Technology), Tasikmalaya Jawabarat

REFERENSI

[1] Agusta, Y. (2007). K-means - Penerapan, Garcia, E.P.I. dan P.M. Mora. 2011. Model Prediction of Academic Performance for First Year Students. IEEE Computer Society. 10 Oktober 2014 [2] Permasalahan dan Metode Terkait. Jurnal Sistem dan Informatika Vol. 3 (Februari 2007): 47-60. [3] Berkhin, Pavel. Survey on clustering data mining techniques, http://www.ee.ucr.edu/~barth/EE24 2/clustering_survey.pdf, 20 Oktober 2014. [4] Gorunescu, F. (2011). Data Mining Concepts,Models And Techniques. Verlag Berlin Heidelberg: Springer [5] Kabakchieva, D. 2012. Student Performance Prediction by Using data Mining Classification Algorithms. IJCSMR. Vol 1 Issue 4: 86-690, 15 Oktober 2014. [6] Kusrini, & Luthfi, T. E. (2009). Algoritma Data Mining. Yogyakarta: Penerbit Andi [7] Kusumadewi, Sri., dan Purnomo, Hari. 2010. Aplikasi Logika Fuzzy Untuk Pendukung Keputusan Jakarta, Graha Ilmu [8] Prasetyo, Eko. (2014). Data Mining Mengolah Data Menjadi Informasi Menggunakan Matlab.Yogyakarta: Andi. [9] Pudjo Widodo, P.Trias Handayanto, R.Herlawatni. (2013). Penerapan Data Mining Dengan Matlab. Bandung Rekayasa Sains [10] Santosa, B. (2007). Data Mining: Teknik Pemanfaatan Data untuk Keperluan Bisnis. Yogyakarta: Graha Ilmu. [11] Santoso, S. (2010). Statistik Multivariat. Jakarta: Elex Media Komputindo. [12] Shovon, Md.H.I. dan M. Haque. (2012). Prediction of Student Academic Performance by an Application of K-Means clustering Algorithm. IJARCSSE. Volume 2 Issue 7: 353-355, 20 Juli 2014 [13] Slameto, (2010). Belajar & FaktorFaktor yang Mempengaruhi.Jakarta:Rineka [14] William, Graham, Data Mining Cluster, 20 Oktober 2014 http://datamining.anu.edu.au/stude nt/math3346_2005/ 050809- maths3346-clusters-2x2.pdf [15] Wahono, R. S. (2012, 06 18). Kiat Menyusun Alur Latar Belakang Masalah Penelitian. Dipetik 02 13, 2013 dari http://romisatriawahono.net/: http://romisatriawahono.net/2012/0 6/18/kiat-menyusun-alur-latarbelakang-masalah-penelitian/ [16] Wahono, R. S. (2012, 08 07). Kiat Menyusun Kerangka Pemikiran Penelitian. Dipetik 05 27, 2013, dari http://romisatriawahono.net: http://romisatriawahono.net/2012/0 8/07/kiat-menyusun-kerangkapemikiran-penelitian/