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p-Index From 2021 - 2026
0.408
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All Journal Engineering, Mathematics and Computer Science Journal (EMACS)
Charisma, Rifqi Alfinnur
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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Breast Cancer Diagnosis Based on a Hybrid Genetic Algorithm and Neural Network Architecture Charisma, Rifqi Alfinnur; Maulina, Ayu
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.13409

Abstract

Breast cancer is one of the diseases with a high prevalence and is a leading cause of death among women. Early detection is crucial in improving patient survival rates. However, a major challenge in diagnosis using machine learning methods is the high dimensionality of the data, which can lead to overfitting and reduced interpretability of the model. This study proposes a new approach to improve breast cancer prediction accuracy by using a combination of Genetic Algorithm + Neural Network (GA + NN). The dataset used is the Breast Cancer Wisconsin (Diagnostic) Data Set, consisting of 569 samples with 32 numerical features that describe the characteristics of tumor cells. The experimental results show that the GA + MLP method achieved the highest accuracy of 99.42%, outperforming the benchmark model using PCA and logistic regression with an accuracy of 97.37%. This approach demonstrates that GA-based feature selection can improve prediction accuracy while reducing model complexity, making it more efficient for medical applications.
Gender Classification Using Keystroke Dynamics: Enhancing Performance with Feature Selection and Random Forest Maulina, Ayu; Charisma, Rifqi Alfinnur
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 3 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i3.13445

Abstract

The purpose of this study is to improve gender categorization by examining the usage of keyboard dynamics, with enhanced model performance through data standardization and appropriate feature selection. Features including gender, age, handedness, language, education, and metrics measuring typing behavior like mean_latency, std_latency, and frequency are all included in the dataset. Correlation analysis served as the foundation for the feature selection procedure, which is essential for effective model training, and data normalization was performed to guarantee consistency among the characteristics that were chosen. Because of its stability and capacity to handle complicated data, the Random Forest classifier was selected. The findings demonstrate that the Random Forest model achieved an accuracy of 95% and an F1-score of 95% when using all features, and 82% accuracy with an F1-score of 82.5% when using only the selected features. The results emphasize how important it is to choose the appropriate characteristics and standardize the data in order to increase predictive accuracy. By showcasing keystroke dynamics' capacity for gender categorization, this study advances the area and creates opportunities for further research in user experience improvement, digital service customization, and online behavioral analysis. Overall, the study emphasizes the importance of feature engineering, normalization, and model tuning for achieving accurate and reliable classification outcomes.
Co-Authors Maulina, Ayu