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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Mobile and Forensics Control Systems and Optimization Letters
Dimas Chaerul Ekty Saputra
Telkom University
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Library & Information Science Neuroscience

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Resource-Efficient Optimization for Multi-Class Hematological Diagnosis: A Hybrid BPSO-Extra Trees Approach with Data Imbalance Handling Dimas Chaerul Ekty Saputra; Zahid Abdullah Nur Mukhlishin; Affifah Mutiara Pertiwi; Mochammad Zulfikar Alfany; Irianna Futri; Raksmey Phann
Mobile and Forensics Vol. 8 No. 1 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v8i1.15703

Abstract

Complete Blood Count (CBC) remains the cornerstone for initial screening of hematological disorders, yet manual interpretation is often challenged by overlapping biological patterns and substantial inter-patient variability. Although machine learning approaches have demonstrated promise for automated diagnosis, many existing studies prioritize classification accuracy while neglecting computational efficiency and the persistent class imbalance inherent in medical datasets. This study develops a lightweight yet effective diagnostic framework for classifying nine hematological conditions using routine CBC parameters. Evaluated on a public dataset of 1,281 records from Kaggle, the proposed model is benchmarked against standard Random Forest, XGBoost, and Support Vector Machine (SVM) classifiers. The approach integrates the Synthetic Minority Oversampling Technique (SMOTE) to mitigate class imbalance, and Binary Particle Swarm Optimization (BPSO) to identify a compact and clinically informative feature subset of exactly 6 parameters, referred to as a clinical fingerprint, optimized for the Extra Trees classifier. Evaluated using ten-fold cross-validation, the BPSO-Extra Trees model achieved an average accuracy of 87.43 percent and an F1 score of 82.75 percent, while demonstrating superior resource efficiency with peak memory consumption of only 0.249 MB, corresponding to a 46.6 percent reduction compared with the standard Random Forest baseline. These findings confirm that swarm intelligence optimized models can effectively balance diagnostic performance with extreme computational frugality, enabling the potential deployment of accurate hematology-based decision support systems on portable devices and in resource-limited laboratory environments.
An Extreme Gradient Boosting for Blood Disease Classification Using Hematological Parameters: A Comparative Evaluation with Ensemble and Non-Ensemble Models Dimas Chaerul Ekty Saputra; Vessa Rizky Oktavia; Irianna Futri; Affifah Mutiara Pertiwi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i4.31659

Abstract

The early detection of hematological disorders remains challenging because many conditions share similar clinical characteristics and show substantial variation in laboratory measurements. Existing machine learning systems often struggle to maintain consistent accuracy in multi-class settings with imbalanced data. The research contribution is a multi-class diagnostic framework that identifies nine hematological disease categories using only routine laboratory parameters, supported by a leakage-free evaluation protocol and a comprehensive comparison across baseline classifiers. The proposed solution uses an extreme gradient boosting model as the primary classifier and evaluates it against support vector machine, random forest, and extra trees. The method includes data cleaning and numerical standardization, and class balancing with the Synthetic Minority Oversampling Technique applied only to the training subset within each fold of ten-fold cross-validation to prevent optimistic bias. Model performance is assessed using accuracy, precision, recall, and F1-score, together with computational efficiency measured through processing time and memory usage. The results show that the extreme gradient boosting model achieves the best overall performance, with an average accuracy of 98.67%, precision of 98.80%, recall of 98.67%, and an F1-score of 98.66%. It also demonstrates efficient memory usage and shorter processing time compared with the other tested methods. The competing models perform adequately but exhibit higher variability and weaker recognition for minority classes. In conclusion, these findings indicate that extreme gradient boosting provides an accurate and efficient approach for hematology-based multi-class disease classification when evaluated under a strict, leakage-free resampling protocol.
Broad Learning System: A Derivation-Based Mathematical Formulation Dimas Chaerul Ekty Saputra; Dyah Putri Rahmawati; Affifah Mutiara Pertiwi; Muhammad Ijaz Shafarin; Kharisma Monika Dian Pertiwi; Thinzar Aung Win; Irianna Futri; Pima Hani Safitri
Control Systems and Optimization Letters Vol 4, No 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i1.294

Abstract

Broad Learning System is a wide learning framework that constructs nonlinear feature representations while enabling efficient model training through analytical solutions. This paper presents a derivation-based formulation of Broad Learning System that explains the mathematical structure underlying the learning process. The model constructs an expanded feature representation through feature mapping nodes followed by enhancement nodes that further enrich the learned representation. The learning problem is then expressed as a linear model in the constructed feature space, and the output weights are obtained using ridge regularized least squares optimization. This formulation allows the training process to be solved directly using matrix operations without iterative gradient based procedures. In addition, an incremental learning mechanism is introduced to enable efficient parameter updates when new samples or additional nodes are incorporated into the model. The presented formulation highlights how Broad Learning System combines nonlinear feature construction with computationally efficient closed form learning, providing a clear theoretical interpretation of the learning process.
Co-Authors Affifah Mutiara Pertiwi Dyah Putri Rahmawati Irianna Futri Irianna Futri Kharisma Monika Dian Pertiwi Mochammad Zulfikar Alfany Muhammad Ijaz Shafarin Pima Hani Safitri Raksmey Phann Thinzar Aung Win Vessa Rizky Oktavia Zahid Abdullah Nur Mukhlishin