Information and Communication Technology Competencies Clustering For Students For Vocational High School Students Using K-Means Clustering Algorithm - Muhammad Faisal; Nurdin Nurdin; Fajriana Fajriana; Zahratul Fitri (Master Program of Information Technology, Universitas Malikussaleh, Aceh Utara, Indonesia, 2022)
The k-Means Clustering algorithm is intended to partition data into one or more groups, where data that has similarities in one group and data has differences in another. Information and Communication Technology (ICT) Competency data clustering in educational units is considered necessary to facilitate educational facilitation based on the differences in student abilities, determine advanced ICT guidance groups and become a reference in determining the place of Industrial Work Practices (Prakerin). This study aims to find out how the K-Means Clustering algorithm can be applied in clustering the ICT competencies of students at the State Vocational High School (SMK) 3 Lhokseumawe. The benefits generated in this study are in the form of visualization of data clustering that can help teachers and school management in formulating ICT policies at SMKN 3 Lhokseumawe. The data used in this study is the Information and Communication Technology (ICT) competency test score data for the 2021/2022 academic year. The data was obtained through a competency test process that refers to the Minister of Education and Culture Regulation Number 45 of 2015 concerning the Role of ICT/KKPI Teachers in the Implementation of the 2013 Curriculum where ICT competence includes the skills to search, store, process, present and disseminate data and information. Data processing in this study uses the K-means Clustering method and the RapidMiner application. Data processing using the RapidMiner application starts with data preparation, determining the number of clusters, and configuring the method. This study uses 3 (three) cluster configurations, namely the Very Competent, Competent, and Less Competent clusters. Testing data processing using the RapidMiner application resulted in 80 (eighty) students in cluster_0 with a Very Competent rating, 64 (sixty-four) students in cluster_1 with a Competent rating, and 10 (ten) students in cluster_2 with a Less Competent rating.