Implementation of Data Mining Using the K-Nearest Neighbor Method to Determine the feasibility of a lecturer's functional promotion

Andreas Theo Pilus Alista Teles Siahaan; Mardi Turnip

doi:10.47709/cnahpc.v4i1.1242

Authors

Andreas Theo Pilus Alista Teles Siahaan Universitas prima indonesia
Mardi Turnip Universitas Prima Indonesia, Indonesia

DOI:

10.47709/cnahpc.v4i1.1242

Keywords:

datamining, K-NN, lecturer, Rank, Promotion

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Abstract

As we know now, every lecturer is obliged to determine the use of the momebase for managing the national lecturer identification number, after getting this, the lecturer concerned can already apply for an academic rank. The data that will be processed in this system is where every lecturer has legal rules to propose academic ranks. The data tested are expert assistant lecturers, lectors (L) lectors are divided into 2 lectors 200 (coordinators/III-C) and 300 lectors (administrators (TKT-1/III-D), head lectors are divided into 3 coaches/(IV-A ) kum 400, supervisor of TKT-1/(IVB) kum 550, main coach of junior/(IV-C) 700, professor of intermediary main coach/(IV-D), KEY Advisor/(IV-E). has shown results by displaying data from lecturers who are eligible to apply for a rank using the K-NN method.

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References

R. A. Pangestu, S. Rudiarto, and D. Fitrianah, “Aplikasi Web Berbasis Algoritma K-Nearest Neighbour Untuk Menentukan Klasifikasi Barang Studi Kasus : Perum Peruri,” vol. 2, no. 1, 2018.

Charles Tandian, Yonata Laia, Andi Saputra, 2019, Penerapan Data Mining dalam Memprediksi Pemenang Klub Sepak Bola Pada Ajang Liga Champion dengan Algoritma C. 45. Vol 2, No 2.

B. Sawit, S. Bss, and M. Metode, “Data Mining Untuk Memprediksi Hasil Produksi Buah Sawit Pada Pt Bumi Sawit Sukses (Bss) Menggunakan Metode K-Nearest Neighbor”, pp. 198–207, 2019.

H. B. Suhartini1, “Klasifikasi Algoritma K-Nearest Neighbor Berbasis Particle Swarm Optimization Untuk Kelayakan Bantuan Rehabilitasi Rumah Tidak Layak Huni Pada Desa Lenek Duren Kecamatan Aikmel Kabupaten Lombok Timur Suhartini1,Hariman,” vol. 2, no. 2, pp. 79-85, 2019.

Yahya, “Prediksi Jumlah Penggunaan BBM Perbulan Menggunakan Algoritma Decition Tree(C4.5),” vol. 1, no. 1, pp. 56–63, 2018.

M. Rivki and A. M. Bachtiar, “Implementasi Algoritma K-Nearest Neighbor Dalam Pengklasifikasian Follower Twitter Yang Menggunakan Bahasa Indonesia,” J. Sist. Inf., vol. 13,no. 1, p. 31, 2017.

Y. A. Setianto, K. Kusrini, and H. Henderi, “Penerapan Algoritma K-Nearest Neighbour Dalam Menentukan Pembinaan Koperasi Kabupaten Kotawaringin Timur,” Creat. Inf. Technol. J., vol. 5, no. 3, p. 232, 2019.

U. B. Rahayu, U. Islam, N. Sunan, and G. Djati, “Penerapan Algoritma K-Nearest Neighbor Dan Algoritma Simple Multi Attribute Rating Technique Untuk Menentukan Strategi Penjualan Pada Pt. Inti (Persero ),” 2016.

B. SAWIT, S. BSS, AND M. METODE, “Data Mining Untuk Memprediksi Hasil Produksi Buah Sawit Pada Pt Bumi Sawit Sukses (Bss) Menggunakan Metode K-Nearest Neighbor,” PP. 198–207, 2019.

M. Syukri Mustafa , I Wayan Simpen 2019. Implementasi Algoritma K-Nearest Neighbor (KNN) Untuk Memprediksi Pasien Terkena Penyakit Diabetes Pada Puskesmas Manyampa Kabupaten Bulukumba.