Pengelompokkan Data Akademik Menggunakan Algoritma K-Means Pada Data Akademik Unissula
Abstract
A Higher education in the digital era as it is now common to use IT technology (Information Technology) in supporting their daily activities, but the use of IT raises a problem that is serious enough if there is no support and no further management this is only produce a data noise , Sultan Agung Islamic University (Unissula) has implemented an IT-based academic information system, in use of this information systems by time this systems produce a lot of data in the unissula academic database and this data is monotonous data and not clustered or also called data noise data that overlap without any benefit and information further in it, the purpose of this study is to solve the problem of these data into student performance data based on the GPA from semester 1 to semester 4 and make it to be a best data to support an alternative decision by the leader, this study uses the method of datamining and k-means algorithms, k-means algorithm is very good to be used as a solution for problems related to clustering, k-means algorithm is an algorithm that is unsupervised and the data can be adjusted by its self according to its class, the results of this study are a decision support system for grouping academic data in the form of dashboard information systems.
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Sutrisno, Afriyudi, and Widiyanto, Penerapan Data Mining Pada Penjualan Menggunakan Metode Clustering Study Kasus Pt . Indomarco, Penerapan Data Min. Pada Penjualan Menggunakan Metod. Clust., vol. Vol.x No.x, no. Data Mining, pp. 1 11, 2013.
X. Wu and R. Srihari, New ν-support vector machines and their sequential minimizationrnalgorithm, Twent. Int. Conf. Mach. Learn., 2003.
Yudi Agusta, K-Means Penerapan, Permasalahan dan Metode Terkait, J. Sist. dan Inform., vol. 3, no. 11, pp. 47 60, 2007.
J. O. Ong, Implementasi Algoritma K-Means Clustering Untuk Menentukan Strategi Marketing, J. Ilm. Tek. Ind., vol. 12, no. 1, pp. 10 20, 2013.
M. Kaur and S. Kang, Market Basket Analysis: Identify the Changing Trends of Market Data Using Association Rule Mining, Procedia Comput. Sci., vol. 85, no. Cms, pp. 78 85, 2016.
Y. C. Chen and C. T. Su, Distance-based margin support vector machine for classification, Appl. Math. Comput., vol. 283, pp. 141 152, 2016.
H. Jia, Large-Scale Data Classification Method Based on Machine Learning Model, Int. J. Database Theory Appl., vol. 8, no. 2, pp. 71 80, 2015.
T. Li, Y. Chen, X. Mu, and M. Yang, An improved fuzzy k-means clustering with k-center initialization, 3rd Int. Work. Adv. Comput. Intell. IWACI 2010, vol. 1009, pp. 157 161, 2010.
H. Islam and M. Haque, An Approach of Improving Student s Academic Performance by using K-means clustering algorithm and Decision tree, Int. J. Adv. Comput. Sci. Appl., vol. 3, no. 8, pp. 146 149, 2012.
Y. Prastyo, Pembagian Tingkat Kecanduan Game Online Menggunakan K-Means Clustering Serta Korelasinya Terhadap Prestasi Akademik, Elinvo (Electronics, Informatics, Vocat. Educ., vol. 2, no. 2, p. 138, 2017.
A. Singh, A. Yadav, and A. Rana, K-means with Three different Distance Metrics, Int. J. Comput. Appl., vol. 67, no. 10, pp. 13 17, 2013.
A. M. Fahim, A. M. Salem, F. A. Torkey, and M. A. Ramadan, Efficient enhanced k-means clustering algorithm, J. Zhejiang Univ. Sci., vol. 7, no. 10, pp. 1626 1633, 2006.
DOI: http://dx.doi.org/10.26623/transformatika.v18i1.2277
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