Implementation of the K-NN method to determine student majors at SMAN 2 Manokwari
DOI:
https://doi.org/10.33379/gtech.v7i1.1905Keywords:
School Selection, Clasification, K-NN, Confuszion MatrixAbstract
Today, in the revolutionary 4.0 era, institutions desperately need an inflating system that could support technological advances in school-schools, in order to make it easier for school teaching processes, which at present are not a few schools implementing online systems, including the process of receiving new students. Therefore, SMAN 2 Manokwari will need a system of tailor-making to assist the curriculum in determining student majors according to its interests, values, and abilities to SMAN 2 manokwari in new student matters, can use the classified data mining, the method used in the research, k-nn algorithm. After conducting a trial using confuszion matrix on k-nn algorithm can result in an accuracy value of 79% on k=9
References
Ds, AlEd SuiaEyah, r, ild., P.-D. (2011). Landasan Pendidikan (D. & Z. J. J (ed.)). I. Bmi l,ls Ra)a Ko@t€tBmi Jaw Rl l0No- 5A Barjamasir 70rt9.
Ibeng, P. (2022). √ Pendidikan : Pengertian, Tujuan, Fungsi Menurut Para Ahli. PENDIDIKAN.CO.ID. https://pendidikan.co.id/pengertian-pendidikan/
Milzam, A., Hidayat, N., & Mahfud, moch. cholil. (2018). Sistem Pakar Diagnosis Penyakit Pada Sapi Menggunakan Metode Dempster-Shafer Berbasis Android. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(10), 3767–3770.
Mutiara, I. dan A. (2015). Penerapan K-Optimal Pada Algoritma Knn Untuk Prediksi Kelulusan Tepat Waktu Mahasiswa Program Studi Ilmu Komputer Fmipa Unlam Berdasarkan Ip Sampai Dengan Semester 4. Klik - Kumpulan Jurnal Ilmu Komputer, 2(2), 159–173.
Rahayuningtyas, C., & Winarso, D. S. (2019). Implementasi Algoritma k-Nearest Neighbor untuk Penjurusan Siswa SMA. CAHAYAtech, 6(2), 50. https://doi.org/10.47047/ct.v6i2.10
Rahmat Dian Nugraha, A., Auliasari, K., & Agus Pranoto, Y. (2020). IMPLEMENTASI METODE K-NEAREST NEIGHBOR (KNN) UNTUK SELEKSI CALON KARYAWAN BARU (Studi Kasus : BFI Finance Surabaya). JATI (Jurnal Mahasiswa Teknik Informatika), 4(2), 14–20. https://doi.org/10.36040/jati.v4i2.2656
Salam, A., Zeniarja, J., Septiyan, R., Khasanah, U., Studi, P., Informatika, T., Komputer, F. I., Nuswantoro, U. D., & Analysis, S. (2018). ANALISIS SENTIMEN DATA KOMENTAR SOSIAL MEDIA FACEBOOK DENGAN K-NEAREST NEIGHBOR ( STUDI KASUS PADA AKUN JASA. 480–486.
Sihotang, J. H., Suparman, A. R., & Niken, C. (2018). Studi Perbandingan Model Pembelajaran Dan Jenis Kelamin Terhadap Hasil Belajar Kognitif Peserta Didik Kelas X IPA SMA Negeri 2 Manokwari. Jurnal Nalar Pendidikan, 6(2), 120–127. https://ojs.unm.ac.id/nalar/article/view/7086
Sulistiyo, A. (2014). Penentuan Jurusan Sekolah Menengah Atas Menggunakan Metode K-Nearest Neighbor Classifier Pada SMAN 16 Semarang. Fasilkom Udinus, 1(1), 1–5.
Tanjungpinang, D. I. W. (2021). Online ISSN 2746-8461. 250–256.
Yani, S., Jumeilah, F. S., & Kadafi, M. (2020). Algoritma K-Nearest Neighbor Untuk Menentukan Kelayakan Keluarga Penerima Bantuan Pangan Non Tunai (Studi Kasus : Kelurahan Karya Jaya). Journal of Information Technology Ampera, 1(2), 75–87. https://doi.org/10.51519/journalita.volume1.isssue2.year2020.page75-87
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