Grouping of Student Learning Interest Data after the Pandemic at SMK Abdi Negara Binjai Using the K-Means Algorithm Clustering Method

Authors

  • Ivan Candra Dinata STMIK Kaputama Binjai, Indonesia
  • Relita Buaton STMIK Kaputama Binjai, Indonesia
  • Novriyenni STMIK Kaputama Binjai, Indonesia

DOI:

https://doi.org/10.55227/ijhet.v1i3.75

Keywords:

Clustering, Data_Mining, Interest_Learning_Students

Abstract

The online learning system is a learning system without face to face directly between teachers and students but is carried out online using the internet network. This is in accordance with the regulation of the Minister of Education and Culture of the Republic of Indonesia regarding Circular Letter Number 4 of 2020 concerning the Implementation of Educational Policies in the Emergency Period for the Spread of Corona Virus Disease (COVID-19). Abdi Negara Vocational School is one of the schools in Binjai City that carries out offline and online learning for its students, learning began in March 2020 when COVID-19 began to hit Binjai City. The COVID-19 pandemic has had many impacts on the state of society, one of which is in the field of education. All educational institutions are trying hard to maximize their respective ways of learning according to the circumstances of their students. Abdi Negara Vocational School which follows government regulations through the Minister of Education and Culture of the Republic of Indonesia also carries out online learning, with the aim that teachers, staff and students are not infected with COVID-19 and can break the chain of spreading the virus. With these conditions, SMK Abdi Negara Binjai needs to build a system that can classify student learning interests, so that it can be used as material for consideration and evaluation of student learning outcomes. Data grouping can apply the Data Mining process with the K-Means algorithm Clustering method which is a process of processing very large amounts of data using statistical, mathematical methods, to utilizing Artificial Intelligence technology to produce a data group. The system is designed with the MATLAB R2014a programming application, after testing with the system the results are that in group 1 there are 836 data, group 2 there are 178 data and group 3 there are 91 data with a total of 1105 student data from the questionnaire results on August 31, 2022 .

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References

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Edward, Setiawan. 1994. Programming with C/C++ and Numerical Applications. Erlangga. Jakarta.

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Published

2022-09-21

How to Cite

Ivan Candra Dinata, Relita Buaton, & Novriyenni. (2022). Grouping of Student Learning Interest Data after the Pandemic at SMK Abdi Negara Binjai Using the K-Means Algorithm Clustering Method. International Journal of Health Engineering and Technology (IJHET), 1(3). https://doi.org/10.55227/ijhet.v1i3.75