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All Journal Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science
Amelia Ritahani Ismail
International Islamic University Malaysia
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Systematic Review on Missing Data Imputation Techniques with Machine Learning Algorithms for Healthcare Amelia Ritahani Ismail; Nadzurah Zainal Abidin; Mhd Khaled Maen
Journal of Robotics and Control (JRC) Vol 3, No 2 (2022): March
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v3i2.13133

Abstract

Missing data is one of the most common issues encountered in data cleaning process especially when dealing with medical dataset. A real collected dataset is prone to be incomplete, inconsistent, noisy and redundant due to potential reasons such as human errors, instrumental failures, and adverse death. Therefore, to accurately deal with incomplete data, a sophisticated algorithm is proposed to impute those missing values. Many machine learning algorithms have been applied to impute missing data with plausible values. However, among all machine learning imputation algorithms, KNN algorithm has been widely adopted as an imputation for missing data due to its robustness and simplicity and it is also a promising method to outperform other machine learning methods. This paper provides a comprehensive review of different imputation techniques used to replace the missing data. The goal of the review paper is to bring specific attention to potential improvements to existing methods and provide readers with a better grasps of imputation technique trends.
Comparison of Swarm Intelligence Algorithms for High Dimensional Optimization Problem Samar Bashath; Amelia Ritahani Ismail
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 1: July 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i1.pp300-307

Abstract

High dimensional optimization considers being one of the most challenges that face the algorithms for finding an optimal solution for real-world problems. These problems have been appeared in diverse practical fields including business and industries. Within a huge number of algorithms, selecting one algorithm among others for solving the high dimensional optimization problem is not an easily accomplished task. This paper presents a comprehensive study of two swarm intelligence based algorithms: 1-particle swarm optimization (PSO), 2-cuckoo search (CS).The two algorithms are analyzed and compared for problems consisting of high dimensions in respect of solution accuracy, and runtime performance by various classes of benchmark functions. 
Enhancing Federated Learning for Imbalanced Medical Image Classification through Adaptive Tuning and Autoencoder-Based Reconstruction Nadzurah Zainal Abidin; Amelia Ritahani Ismail; Cut Amalia Saffiera; Nurul A. Emran; Zammarah Nuha Abdullah
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 3 (2026): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i3.15266

Abstract

Medical image classification has advanced significantly through deep learning techniques, yet its performance remains limited by class imbalance and decentralized data silos commonly found in healthcare settings. These issues reduce model sensitivity to rare but clinically important cases, and standard Federated Learning (FL) further struggles under non-independent and identically distributed (non-IID) data. To address this, an enhanced federated model integrating unsupervised autoencoder-based reconstruction and adaptive tuning is proposed. The research contribution is an enhanced FL model that improves minority-class detection and overall classification performance under imbalanced medical image distributions, while remaining applicable across decentralized healthcare data sources. The method incorporates an autoencoder to compute reconstruction error, enabling emphasis on underrepresented samples, while adaptive tuning dynamically adjusts local hyperparameters and global aggregation weights based on sample difficulty. This integration strengthens minority-class learning without requiring additional labels or altering the decentralized structure. Experimental evaluations were conducted using two benchmark medical image datasets across three induced imbalance ratios (1:10, 1:5, 1:2) for RetinaMNIST and naturally induced imbalance for PneumoniaMNIST dataset. Results show that under severe imbalance (1:10), the enhanced model improves minority-class recall by 59.6%, F1-score by 33.9%, and AUC-ROC by 13.3% compared to standard FL. At 1:5 imbalance, recall increases by 41.3% and F1-score by 26.5%, with accuracy gains up to 6.0%. Even under mild imbalance (1:2), the model maintains consistent improvements, achieving a 26.4% recall gain and 18.9% increase in F1-score. The performance of the enhanced model was further evaluated against three baseline FL models such as standard federated learning (FedAvg), FL with GAN augmentation, and FL with standalone autoencoder-based reconstruction. The results consistently confirm that federated learning integrating adaptive tuning and autoencoder-based reconstruction outperform the three baselines FL-models for accuracy, recall, and F1-score. These findings also demonstrate that the enhanced model provides scalable, coordination-free improvement in imbalanced federated medical image classification, offering stronger performance and stability across real-world heterogeneous settings.
Co-Authors Ahsiah Ismail Asmarani Ahmad Puzi Cut Amalia Saffiera Imran Khan Mohd Jais Junaida Sulaiman Mhd Khaled Maen Nadzurah Zainal Abidin Nur Shairah Muhd Affendy Nur ‘Atikah Binti Mohd Ali Nurul A. Emran Samar Bashath Syed Qamrun Nisa Zammarah Nuha Abdullah