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All Journal Journal of Computer System and Informatics (JoSYC) Bulletin of Computer Science Research
Ht. Barat, Ade Ismiaty Ramadhona
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Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering

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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.
Optimisasi VGG16 dengan Transfer Learning dalam Mendeteksi Penyakit Pada Daun Jagung Ht. Barat, Ade Ismiaty Ramadhona; Astuti, Wiwik Sri; Wanto, Anjar; Solikhun, Solikhun
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.631

Abstract

Corn is one of the major agricultural commodities that plays a strategic role in national food security. However, its productivity often declines due to leaf diseases such as Blight, Common Rust, and Gray Leaf Spot. Manual disease detection is considered inefficient and prone to human error, especially on a large scale. This study aims to develop an automated deep learning-based system for accurate classification of corn leaf diseases. The proposed model utilizes the Convolutional Neural Network (CNN) architecture VGG16 with a transfer learning approach. The dataset comprises 1,200 labeled images of corn leaves categorized into four disease classes, obtained from Kaggle. Image augmentation techniques were applied to improve data diversity and enhance model generalization. The performance of VGG16 was compared with VGG16 Baseline architecture and MobileNetV2. Experimental results show that VGG16 with transfer learning achieved the highest classification accuracy of 96.25%, outperforming the baseline VGG16 (92.92%) and MobileNetV2 (84.58%). These findings demonstrate the effectiveness of VGG16-based transfer learning in automating corn leaf disease detection, supporting the implementation of precision agriculture technology.
Co-Authors Agus Perdana Windarto Astuti, Wiwik Sri Poningsih Poningsih, Poningsih Sembiring, Rahmat Widia Siregar, Sandy Putra Solikhun Solikhun, Solikhun Wanto, Anjar