Optimasi Parameter K Pada Algoritma K-NN Untuk Klasifikasi Prioritas Bantuan Pembangunan Desa

Authors

  • Saiful Ulya Universitas Muhammadiyah Kudus
  • M Arief Soeleman Dian Nuswantoro University
  • Fikri Budiman Dian Nuswantoro University

DOI:

https://doi.org/10.33633/tc.v20i1.4215

Keywords:

Data Mining, Klasifikasi, k-Nearest Neighbors, Algoritma Genetika, Optimize Parameter.

Abstract

Klasifikasi adalah proses menemukan model atau fungsi yang menggambarkan dan membedakan kelas atau konsep data. Algoritma k-NN (k Nearest Neighbors) merupakan algoritma klasifikasi berdasarkan pembelajaran dari data yang sudah terklasifiasi sebelumnya. Algoritma k-NN (k Nearest Neighbors) merupakan algoritma yang sangat bagus dalam menangani beberapa kasus, salah satu kelebihan k-NN diantaranya adalah tangguh terhadap data training yang noisy dan sangat efektif apabila data trainingnya besar. Namun terdapat beberapa masalah pada algoritma k-NN diantaranya adalah penentuan nilai k untuk pemilihan jumlah tetangga terdekatnya sangat sulit, karena nilai k sangat peka atau sensitif terhadap hasil klasifikasi. Pada penelitian ini, akan dilakukan pemodelan klasifiasi dengan menggunakan algoritma k-NN yang difokuskan pada proses penentuan nilai k terbaik pada dataset IKG (Indeks Kesulitas Geografis) desa. Pada penelitian ini akan melakukan integrasi algoritma k-NN dengan menentukan nilai k optimal dengan optimize parameters berdasar algoritma genetika.

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Published

2021-02-09

Issue

Vol. 20 No. 1 (2021): Februari 2021

Section

Articles

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