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

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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ARTICLE Published HISTORY

Submitted Date: 2021-12-13
Accepted Date: 2021-12-15
Published Date: 2022-01-06

How to Cite

Siahaan, A. T. P. A. T., & Turnip, M. (2022). Implementation of Data Mining Using the K-Nearest Neighbor Method to Determine the feasibility of a lecturer’s functional promotion. Journal of Computer Networks, Architecture and High Performance Computing, 4(1), 62-68. https://doi.org/10.47709/cnahpc.v4i1.1242