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All Journal Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Prosiding Seminar Nasional Ilmu Komputer, Sosial Sains, Teknik Dan Multi-disiplin Ilmu
Syarifah Yusnaini Putri
Universitas Medan Area
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Implementation of Federated Learning for Disease Classification with Imbalanced Dataset Syarifah Yusnaini Putri; Nia Ramadani; Andre Hasudungan Lubis
Prosiding Seminar Nasional Ilmu Komputer, Sosial Sains, Teknik dan Multi-Disiplin Ilmu Vol. 2 (2026)
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/ikosstemi.v2.148

Abstract

Imbalanced data is one of the main challenges in applying machine learning to stroke disease classification, where the number of stroke patient data is significantly smaller than non-stroke patient data. This condition causes classification models to become biased toward the majority class, resulting in poor detection of stroke cases. In addition, centralized learning approaches raise privacy and security concerns due to the sensitive nature of patient data. Therefore, this study proposes a Federated Learning Random Forest (FL-RF) approach to improve classification performance on imbalanced data while preserving data privacy. The study uses the Cerebral Stroke Prediction – Imbalanced Dataset obtained from Kaggle. The research stages include data preprocessing, semi non-IID data distribution across multiple clients, local training using Balanced Random Forest, and model aggregation within a federated learning environment. Model evaluation was conducted using accuracy, precision, recall, F1-score, confusion matrix, and ROC curve metrics. The results show that the conventional Random Forest achieved an accuracy of 0.9817 but failed to detect the minority class. Meanwhile, the proposed FL-RF model obtained an accuracy of 0.7976 with an improved recall of 0.56 and an F1-score of 0.0910 for the stroke class. These findings indicate that the FL-RF approach is more effective in improving sensitivity toward minority classes compared to conventional Random Forest.  
Evaluasi Recursive Feature Elimination Untuk Klasifikasi Kanker Payudara Menggunakan Berbagai Algoritma Machine Learning Syarifah Yusnaini Putri; Sayuti Rahman; Nia Ramadani; Novalia Aprianti Ginting; Layla Syalsyadilla; Dedi Agustriaman Zebua
Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Vol. 8 No. 1 (2026): Edisi April
Publisher : Universitas Harapan Medan

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Abstract

Early detection of breast cancer requires classification models that are not only accurate but also efficient and interpretable. This study evaluates the effect of Recursive Feature Elimination (RFE) on the performance of several machine learning algorithms for breast cancer classification. The dataset used is the Wisconsin Diagnostic Breast Cancer (WDBC) dataset from the UCI Machine Learning Repository, consisting of 569 samples and 30 numerical features. The research stages include data preprocessing, removal of non-informative attributes, feature standardization using StandardScaler, train-test splitting with an 80:20 ratio, feature selection using Logistic Regression-based RFE, and training and testing of 11 classification algorithms. Model performance was evaluated using accuracy, precision, recall, F1-score, confusion matrix, and Receiver Operating Characteristic (ROC) curve. The results show that before feature selection, Support Vector Machine, Logistic Regression, and Voting Classifier achieved the highest accuracy of 98.25%. After applying RFE, the accuracy of these models decreased slightly to 97.37%, while the number of features was reduced from 30 to 15. Several algorithms, including Nearest Centroid, Naïve Bayes, and AdaBoost, showed improved accuracy after RFE. These findings indicate that RFE does not always improve the best model accuracy, but it can produce a more compact, efficient, and interpretable classification model.
Co-Authors Andre Hasudungan Lubis Dedi Agustriaman Zebua Layla Syalsyadilla Nia Ramadani Novalia Aprianti Ginting Rahman, Sayuti