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All Journal Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Khoirun Nisa
Universitas Harapan Bangsa, Purwokerto
Computer Science & IT Control & Systems Engineering

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Model Comparison of Random Forest and Logistic Regression Algorithms in PCOS Disease Detection Khoirun Nisa; P Purwono; Bala Putra Dewa; Sony Kartika Wibisono
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 4, No 1 (2023): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v4i1.119

Abstract

PCOS or Polycystic Ovary Syndrome is a hormonal imbalance affecting egg cells' growth, making them remain small and not develop into large and mature egg cells to be fertilized by sperm cells. It is an endocrinopathy disease occurred in 10-15% of productive-aged women worldwide. The study aims to find the most suitable algorithm to be used in the optimization of PCOS detection. Thus, a performance comparison between random forest and logistic regression algorithms needs to be conducted in order to find the best performance in terms of accuracy. The research used a dataset containing 40 features. According to comparison results, the random forest algorithm was superior to logistic regression, with an accuracy of 91 %.
Co-Authors Bala Putra Dewa Purwono, Purwono Sony Kartika Wibisono