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All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) Journal of Computer System and Informatics (JoSYC) Jurnal Akuntansi dan Keuangan J-SAKTI (Jurnal Sains Komputer dan Informatika) SWARNA JGEN : Jurnal Pengabdian Kepada Masyarakat
Sembiring, Rahmat Widia
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Journal : Journal of Computer System and Informatics (JoSYC)

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Implementation of Random Forest Optimized with Ant Colony Optimization (ACO) for Breast Cancer Prediction Ht. Barat, Ade Ismiaty Ramadhona; Siregar, Sandy Putra; Poningsih, Poningsih; Windarto, Agus Perdana; Solikhun, Solikhun; Sembiring, Rahmat Widia
Journal of Computer System and Informatics (JoSYC) Vol 6 No 4 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i4.7116

Abstract

Breast cancer is a significant disease impacting women globally, highlighting the necessity for precise and dependable diagnostic models. This study aims to improve breast cancer prediction by optimizing the Random Forest algorithm using Ant Colony Optimization (ACO). This study uses datasets containing various cell characteristics to build and evaluate models. The ACO algorithm is applied to fine-tune the hyperparameters of the Random Forest model and improve its predictive performance. The experimental results showed that the optimized Random Forest model outperformed the baseline model in all evaluation metrics. The optimized model achieved an accuracy of 94.74%, precision of 97.92%, recall 90.38%, an F1 score of 92.93%, and an AUC score of 0, 9449 compared to the basic Random Forest model, with lower scores across all metrics. This improvement highlights the effectiveness of ACOs in improving model performance, especially in reducing false negatives, which are critical for medical diagnosis. This study demonstrates that ACO successfully fine-tunes Random Forest hyperparameters, achieving superior accuracy compared to baseline and outperforming previous optimization methods such as PSO. These findings confirm that the combination of Random Forest and ACO offers a powerful and effective approach to improving the accuracy of breast cancer predictions, making them a valuable tool for clinical decision-making.
Co-Authors Agus Perdana Windarto Deliana, Deliana Friendly, Friendly Harizahayu, Harizahayu Ht. Barat, Ade Ismiaty Ramadhona Muhammad Zarlis, Muhammad Poningsih Poningsih, Poningsih Putra, Purwa Hasan Saifullah Saifullah Sembiring, Zakaria Siregar, Sandy Putra Solikhun Solikhun, Solikhun Surianti, Meily Yusuf, Fadel Zakaria Zakaria