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INDONESIA
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Published by Polilteknik Kesehatan Kemenkes Surabaya
ISSN : -     EISSN : 26568624     DOI : https://doi.org/10.35882/ijeeemi
Core Subject : Health, Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics (IJEEEMI) publishes peer-reviewed, original research and review articles in an open-access format. Accepted articles span the full extent of the Electronics, Biomedical, and Medical Informatics. IJEEEMI seeks to be the world’s premier open-access outlet for academic research. As such, unlike traditional journals, IJEEEMI does not limit content due to page budgets or thematic significance. Rather, IJEEEMI evaluates the scientific and research methods of each article for validity and accepts articles solely on the basis of the research. Likewise, by not restricting papers to a narrow discipline, IJEEEMI facilitates the discovery of the connections between papers, whether within or between disciplines. The scope of the IJEEEMI, covers: Electronics: Intelligent Systems, Neural Networks, Machine Learning, Fuzzy Systems, Digital Signal Processing, Image Processing, Electromedical: Biomedical Signal Processing and Control, Artificial intelligence in biomedical imaging, Machine learning and Pattern Recognition in a biomedical signal, Medical Diagnostic Instrumentation, Laboratorium Instrumentation, Medical Calibrator Design. Medical Informatics: Intelligent Biomedical Informatics, Computer-aided medical decision support systems using heuristic, Educational computer-based programs pertaining to medical informatics
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 12 Documents
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Search results for , issue "Vol. 8 No. 1 (2026): February" : 12 Documents clear
Comparison Between K-Fold Cross Validation And Percentage Split In Decision Tree Algorithms For Anemia Classification Rahmawati, Nanda Putri; Irwan Budiman; Muhammad Itqan Mazdadi; Andi Farmadi; Friska Abadi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.315

Abstract

Anemia is a significant global health challenge characterized by a pathological deficit in hemoglobin concentration, often leading to physiological instability. Accurate clinical diagnosis typically relies on complete blood count (CBC) tests, which provide critical hematological parameters for classification. While machine learning models have demonstrated high efficacy in diagnosing anemia, existing research often relies on static data partitioning strategies that may overlook evaluation reliability and performance stability. This study addresses this gap by shifting the focus from architectural benchmarking to validation robustness, specifically evaluating the C4.5 algorithm's performance across different data-splitting techniques. The research uses a dataset comprising 1,281 clinical records with 14 numerical features and 9 anemia-type labels. To assess stability, two distinct partitioning strategies were implemented: a static Percentage Split (ranging from 60:40 to 90:10) and iterative K-Fold Cross Validation (with K values of 3, 5, 7, 10, and 15). Experimental results demonstrate that the C4.5 algorithm achieved its peak performance with the 90:10 Percentage Split, achieving an average accuracy of 99.46%, precision of 98.32%, and recall of 99.28%. In comparison, the K-Fold (K=10) approach yielded a slightly lower but more stable accuracy of 99.19% with a significantly reduced standard deviation (±0.09), highlighting its reliability for clinical applications. While the high-ratio percentage split maximizes training exposure and predictive potential, the K-Fold method provides a more objective, generalizable benchmark by accounting for the entire data distribution. The study further identifies challenges in classifying minority classes, such as Leukemia with thrombocytopenia, due to inherent data scarcity. Ultimately, this research confirms that the C4.5 algorithm, when paired with an optimal partitioning protocol, remains a robust and highly interpretable solution for clinical anemia screening, outperforming several complex modern architectures
The Effect of Smote-Tomek on the Classification of Chronic Diseases Based on Health and Lifestyle Data Muhammad Adika Riswanda; Friska Abadi; Muhammad Itqan Mazdadi; Mohammad Reza Faisal; Rudy Herteno
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.324

Abstract

Machine learning models for chronic disease prediction are often trained on imbalanced healthcare datasets, where non-disease cases dominate. This condition can lead to misleadingly high accuracy while failing to identify patients with chronic diseases, limiting clinical usefulness. This study aims to analyze the impact of class imbalance on model performance and to evaluate the effectiveness of the SMOTE–Tomek resampling technique in improving chronic disease prediction. This research provides empirical evidence that accuracy alone is insufficient for evaluating healthcare models and demonstrates that imbalance-aware preprocessing is essential for valid and reliable chronic disease detection. Five classification models, such as Support Vector Machine, Random Forest, K-Nearest Neighbors, Gradient Boosting, and XGBoost, were evaluated on a lifestyle-based chronic disease dataset under two conditions: without resampling and with SMOTE–Tomek. Model performance was assessed using accuracy, precision, recall, F1-score, and AUC. Without SMOTE–Tomek, all models failed to detect chronic disease cases, producing near-zero recall and F1-scores despite accuracy exceeding 80%. After applying SMOTE–Tomek, substantial improvements were observed across all models, particularly in recall and AUC. Support Vector Machine achieved the best overall performance, with an accuracy of 92.9%, a precision of 92%, a recall of 93.9%, an F1-score of 0.93, and an AUC of 0.98. The findings confirm that handling class imbalance is a prerequisite for meaningful chronic disease prediction. The consistent increase in recall and AUC across all evaluated models confirms that the improvement stems from enhanced class separability rather than metric inflation. The proposed approach supports more reliable early screening and decision-support systems in preventive healthcare

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