Klasifikasi Teks Mitos dan Fakta Kesehatan Menggunakan Algoritma Random Forest dengan Optimasi Class Weight

Authors

  • Rizky Ananda Putra Universitas Nusantara PGRI Kediri Indonesia
  • Risky Aswi Ramadhani Universitas Nusantara PGRI Kediri Indonesia
  • Julian Sahertian Universitas Nusantara PGRI Kediri Indonesia

DOI:

https://doi.org/10.29407/49rc6579

Abstract

Penyebaran misinformasi kesehatan di ruang digital dapat membangun persepsi ancaman yang keliru pada masyarakat, sehingga diperlukan metode klasifikasi teks. Penelitian ini mengklasifikasikan 200 data teks kesehatan ke dalam kelas mitos 52 data dan fakta 148 data menggunakan algoritma Random Forest. Dataset yang digunakan telah divalidasi oleh pakar medis. Alur penelitian meliputi prapemrosesan teks, ekstraksi fitur TF-IDF, serta pelatihan dan pengujian model dengan berbagai rasio data. Optimasi dilakukan menggunakan parameter class_weight untuk mengatasi ketidakseimbangan data. Hasil pengujian menunjukkan dengan rasio 90:10 mendapat nilai akurasi keseluruhan sebesar 85,00%, dengan f1-score kelas fakta sebesar 0.9032 dan kelas mitos sebesar 0.6667. Kontribusi penelitian ini adalah pembuktian bahwa parameter class_weight mampu menjaga performa klasifikasi kelas minoritas tetap berada di atas ambang batas tebakan acak 0,5000. Hasil ini dapat menjadi referensi pemodelan klasifikasi hoaks kesehatan ke depan.

Keywords:

Klasifikasi Teks, Random Forest, Class Weight, TF-IDF, Informasi Kesehatan

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Published

2026-07-20

Issue

Vol. 10 No. 1 (2026): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2026

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How to Cite

Klasifikasi Teks Mitos dan Fakta Kesehatan Menggunakan Algoritma Random Forest dengan Optimasi Class Weight. (2026). Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 10(1), 141-150. https://doi.org/10.29407/49rc6579