The Implementation of K Means Algorithm for Cluster Majoring to New Students in SMKN 2 of South Tangerang

The Implementation of K Means Algorithm for Cluster Majoring to New Students in SMKN 2 of South Tangerang

• By Munawaroh, Enggar Tyastiwi; Handayani, Rani Irma; Widanengsih, Euis;
• Jurnal Nasional Terakreditasi
• 267
• 164-170

The diversity of majors in vocational schools of SMKN 2 South Tangerang makes some students confuse 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. The K-Means Clustering Method makes it easier to grouping new students’ data. The calculation produces information on the number of students per majoring. Industrial Electronics Engineering has 49 students. Light Vehicle Engineering 95 students. Accounting major has 96 students. Multimedia major has 44 students. And Motorcycle Business Engineering has 36 students

• Katakunci Majoring, Vocational High School, Clustering, K-Means
• URL http://www.polgan.ac.id/jurnal/index.php/sinkron/article/view/10133
• Penerbit ,

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