Analisis Komparatif Algoritma Machine Learning untuk Prediksi Kekambuhan Kanker Payudara Berdasarkan Karakteristik Tumor

Authors

  • Chairunnisa Desti Arzety Universitas Sriwijaya, Palembang, Indonesia
  • Vanya Dwi Nabila Universitas Sriwijaya, Palembang, Indonesia
  • Aprilia Herawati Universitas Sriwijaya, Palembang, Indonesia
  • Allsela Meiriza Universitas Sriwijaya, Palembang, Indonesia
  • Ken Ditha Tania Universitas Sriwijaya, Palembang, Indonesia

DOI:

https://doi.org/10.62411/tc.v25i2.15976

Abstract

Kanker payudara merupakan salah satu penyebab kematian tertinggi pada perempuan di dunia, di mana tantangan utamanya terletak pada risiko kekambuhan (recurrence). Penelitian ini bertujuan untuk membangun model prediksi kekambuhan kanker payudara dengan membandingkan tiga algoritma machine learning, yaitu Logistic Regression, Decision Tree, dan Random Forest, berdasarkan karakteristik tumor dari dataset METABRIC. Tahapan penelitian meliputi pra-pemrosesan data, seleksi fitur klinis, dan pembagian data dengan rasio 80:20. Hasil evaluasi menunjukkan bahwa Logistic Regression memiliki performa terbaik dalam hal akurasi (0,661) dan ROC-AUC (0,689), sementara Random Forest menunjukkan keunggulan pada metrik recall (0,544) yang krusial untuk deteksi pasien berisiko. Analisis feature importance mengidentifikasi bahwa jumlah mutasi genetik (Mutation Count), Nottingham Prognostic Index (NPI), dan ukuran tumor merupakan faktor paling dominan dalam memprediksi kekambuhan. Penelitian ini menyimpulkan bahwa karakteristik biologis tumor memiliki pengaruh signifikan terhadap risiko kekambuhan dan penggunaan machine learning berpotensi besar menjadi sistem pendukung keputusan klinis untuk stratifikasi risiko pasien secara objektif.   Kata kunci - Kanker Payudara, Kekambuhan, Machine Learning, METABRIC, Karakteristik Tumor

Downloads

Published

2026-05-28

Issue

Vol. 25 No. 2 (2026): May 2026

Section

Articles

License

Copyright (c) 2026 Chairunnisa Desti Arzety, Vanya Dwi Nabila, Aprilia Herawati, Allsela Meiriza, Ken Ditha Tania

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

 

License Terms

All articles published in Techno.COM Journal are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). This means:

1. Attribution

Readers and users are free to:

  • Share – Copy and redistribute the material in any medium or format.

  • Adapt – Remix, transform, and build upon the material.

As long as proper credit is given to the original work by citing the author(s) and the journal.

2. Non-Commercial Use

  • The material cannot be used for commercial purposes.

  • Commercial use includes selling the content, using it in commercial advertising, or integrating it into products/services for profit.

3. Rights of Authors

  • Authors retain copyright and grant Techno.COM Journal the right to publish the article.

  • Authors can distribute their work (e.g., in institutional repositories or personal websites) with proper acknowledgment of the journal.

4. No Additional Restrictions

  • The journal cannot apply legal terms or technological measures that restrict others from using the material in ways allowed by the license.

5. Disclaimer

  • The journal is not responsible for how the published content is used by third parties.

  • The opinions expressed in the articles are solely those of the authors.

For more details, visit the Creative Commons License Page:
? https://creativecommons.org/licenses/by-nc/4.0/