DISKRITISASI EQUAL-WIDTH INTERVAL PADA NAIVE BAYES (STUDI KASUS: KLASIFIKASI PASIEN TBC)

Authors

  • Hariyani Hariyani Program Studi Matematika FAST UAD
  • Sugiyarto Surono Program Studi Matematika FAST UAD

DOI:

https://doi.org/10.12928/admathedu.v10i2.20129

Abstract

Klasifikasi Naive Bayes merupakan teknik untuk memprediksi probabilitas keanggotaan suatu kelas dengan menerapkan teorema Bayes. Klasifikasi Naive Bayes akan lebih baik jika menggunakan data yang berbentuk kategorik, sehingga dalam penelitian ini digunakan diskritisasi equal-width interval pada Naive Bayes untuk mengubah data yang berbentuk numerik menjadi kategorik. Tujuan dari penelitian ini adalah untuk menerapkan metode Naive Bayes dengan diskritisasi equal-width interval dalam mengklasifikasi pasien TBC di Puskesmas Sewon 1. Hasil penelitian ini menunjukkan akurasi sebesar 100% dengan perbandingan data training dan data testing sebesar 80%:20% dan 90%:10%, sehingga klasifikasi Naive Bayes dapat dikategorikan baik dalam mengklasifikasi pasien TBC.

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Published

2020-12-15

Issue

Vol. 10 No. 2: Desember 2020

Section

Articles

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