Penerapan K-Nearest Neighbour dalam Penerimaan Peserta Didik dengan Sistem Zonasi

Denni Kurniawan orcid scopus  -  Universitas Budi Luhur, Indonesia
*Ade Saputra  -  Universitas Budi Luhur, Indonesia
Received: 22 Oct 2019; Revised: 8 Nov 2019; Accepted: 9 Nov 2019; Published: 28 Nov 2019.
DOI: https://doi.org/10.21456/vol9iss2pp212-219 View
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Abstract
Admission of new students is a routine activity at the beginning of each new meeting year in all formal educational institutions. At the moment the acceptance of new students uses the zoning system and has been regulated by Permendikbud No. 20 in 2019. This zoning system will accept students where their residence enters through the user area with the school environment. With this Permendikbud the government hopes that there will be an evenness in the quality of education in all schools, so that schools will no longer get the title of superior and non-superior schools. But in a system, the zoning improves anxieties in the school environment. This research supports to help the participating school students will be accepted in accordance with the provisions of the Ministry of Education and Culture. In overcoming problems that arise in the school environment there needs to be a system that can overcome that problem. In this study using the K-Nearest Neighbor (K-NN) method. Where the K-NN method will do the classification of new learners' residence with the school. In determining the classification using the K-NN method used for zoning and non-zoning areas, it is seen based on the closest K value. In finding the optimal value in this study using the Rapidminer application. The optimal high-level test results at K 5 where the value of this K is 83.36%

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Keywords: Data Mining, K-NN, Rapidminer, Zonasi

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  1. Adinugroho, S. dan Y.A. Sari., 2018. Implementasi Data Mining Menggunakan WEKA. 1st ed. Malang: UB Press
  2. Aprillia, Dennis, D.A. Baskoro, Lia, A. dan I.W.S. Wicaksana. 2013. Belajar Data Mining dengan RapidMiner. Jakarta: Remi Sanjaya
  3. Badu, Z.S., 2016. Penerapan Algoritma K-Nearest Neighbor Untuk Klasifikasi Dana Desa.” (November)
  4. Hasanah, Riyan, L., Hasan, M. dan Pangesti, W.E., 2019. Klasifikasi penerimaan dana bantuan desa menggunakan metode K-NN (K-Nearest Neighbor). TECHNO Nusa Mandiri 16(1):1–6
  5. Inggi, Rahmat, Sugiantoro, B. dan Prayudi, Y., 2018. Penerapan system development life cycle (SDLC) dalam mengembangkan framework audio forensik. SemanTIK 4(2):201–36
  6. Kartika, Irjaya, J., Santoso, E. dan Sutrisno, 2017. Penentuan siswa berprestasi menggunakan metode K-Nearest Neighbor dan Weighted Product (Studi Kasus : SMP Negeri 3 Mejayan).” 1(5)
  7. Mendikbud, 2018. Peraturan Menteri Pendidikan Dan Kebudayaan Republik Indonesia No 51 Tahun 2018.” Retrieved June 12, 2019 ( https://jdih.kemdikbud.go.id/arsip/PERMENDIKBUD NOMOR 51 TAHUN 2018.pdf)
  8. Noviansyah, Reza, M., Rismawan, T. dan Midyanti, D.M., 2018. Penerapan data mining menggunakan metode k-nearest neighbor untuk klasifikasi indeks cuaca kebakaran berdasarkan data AWS (Automatic Weather Station) Studi Kasus : Kabupaten Kubu Raya. Jurnal Coding, Sistem Komputer Untan, 06(2):48–56
  9. Novita, R., 2016. Teknik Data Mining : Algoritma C 4.5. 1–12
  10. Saifudin, A., 2018. Metode data mining untuk seleksi calon mahasiswa pada penerimaan mahasiswa baru di Universitas Pamulang. Jurnal Teknologi 10(1):25–36
  11. Sumarlin, S., 2015. Implementasi algoritma k-nearest neighbor sebagai pendukung keputusan klasifikasi penerima beasiswa PPA dan BBM. Jurnal Sistem Informasi Bisnis 5(1):52–62
  12. Umaedi, H. dan Siswantari, 2016. Manajemen Berbasis Sekolah. Edisi 1. edited by D. Setiawan and A. Suroso. Tangerang Selatan: Universitas Terbuka

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Last update: 2021-03-04 05:08:21

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