PENGGUNAAN METODE KLASTERISASI K-MEANS DALAM MENENTUKAN MINAT JURUSAN PADA PROSES PENERIMAAN PESERTA DIDIK BARU

  • Tajrin Tajrin Universitas Prima Indonesia
  • Kevin Agape Tampubolon Universitas Prima Indonesia
  • Ronasib Haryanto Syahputra Universitas Prima Indonesia
  • Piltodam Luhut Gunawan Silaban Universitas Prima Indonesia

Abstract

Al Manar High School is one of the schools under the auspices of the Al Munawwarah Al Manar Islamic Education Foundation which is located in Medan City. Al Munawwarah has several educational units, namely, SMA, Aliyah, SMP, MTs and SD. Where every year Al Manar High School always accepts 200 new students each year. This results in schools not utilizing PPDB data properly. However, data utilization for strategic needs for both promotion and marketing evaluation has not been fully carried out using existing data. One way to make it easier to determine marketing promotion is with the K-Means algorithm. This research will recommend the determination of majors for new students of Al -Manar High School by processing data on written test exam scores on new students, student majors consist of 2 namely Science and Social Sciences while the exam variables carried out consist of mathematics, Indonesian language, English, Science and Social Sciences, this research uses new student data as many as 145 students. With this research, the percentage level of grouping new student majors is higher, based on selected attributes with the K-Means Clustering algorithm. The test resulted in a science grouping of 113 students and a social science grouping of 35 students and resulted in an accuracy rate of 52.9%.


 

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Published
2023-12-27
How to Cite
TAJRIN, Tajrin et al. PENGGUNAAN METODE KLASTERISASI K-MEANS DALAM MENENTUKAN MINAT JURUSAN PADA PROSES PENERIMAAN PESERTA DIDIK BARU. Jurnal Tekinkom (Teknik Informasi dan Komputer), [S.l.], v. 6, n. 2, p. 576-584, dec. 2023. ISSN 2621-3079. Available at: <https://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/934>. Date accessed: 24 june 2024. doi: https://doi.org/10.37600/tekinkom.v6i2.934.
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Articles