The Implementation of K-Means Algorithm for Cluster Majoring to New Students in SMKN 2 of South Tangerang
DOI:
10.33395/sinkron.v4i1.10133Keywords:
clustering, k-means, majoring, vocational high schoolAbstract
The diversity of majors in vocational schools of SMKN 2 South Tangerang makes some students confuse of their choices. Determination of majors is important because it will affect the academic activities of students. The purpose of the right majoring is so that students can learn optimally, and be able to equip themselves with competency skills according to their talents, interests and abilities when entering the workforce. This study applies the Clustering Method with the K-means Algorithm, to help students determine their majors, also helps the school in clustering majors. Determination of these majors is based on 320 student data with attributes of the National Examination during Junior High School (Mathematics, English, Indonesian, and Science), Registration Pathways, and Gender. Calculations that occur as many as 7 iterations with the K-Means Clustering Method, with the final centroid: C1 = 1.55; 4.08; 3.65; 4.06; 3.36; 1.93. C1 represents the Industrial Electronics Engineering Major, with the results of 49 students. C2 = 1; 3.69; 3.26; 2.55; 3.09; 1.96. C2 represents Light Vehicle Engineering majors, with 95 students. C3 = 1,98; 4,09; 3,60; 2,79; 3,083; 1,94. C3 = 1.98; 4.09; 3.60; 2.79; 3,083; 1.94. C3 represents the Accounting major, with 96 students. C4 = 1.18; 3.90; 3.39; 2.18; 2.02; 1.93. C4 represents the Multimedia major, with a total of 44 students And C5 = 1,055; 3.72; 2; 2,389; 2.86; 1.97. C5 represents the Motorcycle Business Engineering major with 36 students.
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