Cluster Analysis for Learning Style of Vocational High School Student Using K-Means and FUZZY C-MEANS (FCM)

REZA ANDREA, Shinta Palupi, Siti Qomariah

Abstract


The inability of students to absorb the various knowledge conveyed by the teacher is not due to the inability of his understanding and not because the teacher is not able to teach, but rather due to the incompatibility of learning styles (learning style) between students and teachers, so that students feel uncomfortable learning to certain teachers, it occurred also in SMKN 2 Penajam Paser Utara (PPU), research to analyze cluster (group) type of student learning by applying data mining method that is K-means and Fuzzy C-means (FCM). The goal to be achieved is to know the effectiveness of this type of learning clustering on the development of absorptive capacity and improvement of student achievement. In this research, the method used to cluster the learning type with data mining process starting from data cleaning, data selection, data transformation, data mining, pattern evolution, and knowledge (knowledge).

Keywords


data mining, learning style, clustering, FCM, K-means

Full Text:

PDF

References


Dean, J. 2014. Big Data, Data Mining, and Machine Learning Value Creation for Business Leaders and Practitioners. Wiley. New Jersey.

Deporter,B. dan Hernacki,M. 2011. Quantum Learning. Terjemahan Alwiyah Abdurrahman. Kaifa. Bandung.

Ghosh, S. dan Dubey, S.K. 2013. Comparative Analysis of K-Means and Fuzzy C-Means Algorithms. International Journal of Advanced Computer Science and Applications. 4(4): 35-39.

Hasibuan, Z. A. 2007. Metodologi Penelitian Pada Bidang Ilmu Komputer dan Teknologi Informasi : Konsep dan Aplikasi. Fakultas Imu Komputer Universitas Indonesia. Jakarta.

Kamber, H.J. dan Pie, J.M. 2012. Data Mining : Concepts and Techniques.Edisi 3. Morgan Kaufmann. USA.

Ledolter, J. 2013. Data Mining and Business Analytics with R. Wiley. New Jersey.

Lestari, W. 2015. Pemetaan Gaya Belajar Mahasiswa dengan ClusteringMenggunakan Fuzzy C-means. Jurnal Sainstech Politeknik Indonusa Surakarta.

(3): 23-31.

Merliana, N.P.E. 2015. Perbandingan Metode K-Means Dengan Fuzzy C-Means

Untuk Analisa Karakteristik Mahasiswa Berdasarkan Kunjungan Ke Perpustakaan. Tesis. Fakultas Teknik InformatikaUniversitas Atma Jaya, Yogyakarta.

Setiawati. 2008. Education Games. Proumedia. Jakarta.

Wilis, R. 2011. Teori-teori belajar dan Pembelajaran. Erlangga. Jakarta.

Williams, J dan Simoff, J. 2006. Data Mining Theory, Methodology, Technique, and Aplication. Springer Verlag Berlin Heidelberg. Germany.

Winkel, W.S. 2012. Psikologi Pengajaran. Media Abadi. Yogyakarta.

Witten, I.H. Frank, E. dan Hall, M.A. 2011. DataMining Practical Machine Learning Tool and Techniques. Edisi 3. Elsevier Inc. USA.




DOI: http://dx.doi.org/10.17933/jppi.2017.070204

Copyright (c) 2017 Jurnal Penelitian Pos dan Informatika

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Jurnal Penelitian Pos & Informatika

ISSN 2088-9402 (print)| 2476-9266 (online)
Badan Litbang SDM Kemenkominfo
Puslitbang Sumber Daya, Perangkat, dan Penyelenggaraan Pos dan Informatika
Jalan Medan Merdeka Barat No. 9, Lantai 4 Gedung Belakang, Kementerian Komunikasi dan Informatika. Telepon: +62 21 34833640