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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics Narra X
Herlina Dimiati
Department of Pediatrics, Faculty of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Immunology & microbiology Materials Science & Nanotechnology Medicine & Pharmacology Physics Public Health

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Culture-based breast milk and infant gut microbiota profiles among stunted infants: A case-control study from Indonesia Aslinar Aslinar; Herlina Dimiati; Nur Indrawati; Sofia Sofia
Narra X Vol. 4 No. 2 (2026): August 2026 (In Press)
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narrax.v4i2.294

Abstract

Stunting remains a major public health problem in Indonesia. Early-life microbiota may contribute to infant growth through their roles in microbial colonization, immune maturation, intestinal function, and nutrient utilization, but evidence from high-stunting settings in Indonesia remains limited. The aim of this study was to compare culture-based breast milk and infant fecal microbiota profiles between stunted and normal infants in Aceh, Indonesia, and to examine their associations with maternal, postnatal, environmental, and socioeconomic factors. A total of 54 breastfed infants aged 6–11 months were included, consisting of 27 stunted and 27 normal infants matched by age and sex. Breast milk and fecal samples were analyzed using culture-based microbiological methods for bacterial identification and colony-forming unit counts. In breast milk, Streptococcus sp. and Staphylococcus sp. were the predominant bacteria, with higher detection frequencies among mothers of stunted infants. In fecal samples, Escherichia coli was the most frequently detected bacterium in both groups, while Staphylococcus aureus and Clostridium innocuum were significantly more common among stunted infants. Breast milk bacterial colony counts were significantly higher in the stunted group, whereas fecal colony counts were numerically higher but not statistically significant. Poor household sanitation was more frequent among stunted infants. Exploratory analyses among stunted infants showed that maternal height was associated with Anaerococcus prevotii, while maternal education was associated with selected fecal bacteria, including Salmonella choleraesuis, Proteus mirabilis, and Eggerthella lenta. These findings suggest culture-detectable differences in breast milk and infant fecal microbiota between stunted and normal infants. Given the small sample size, confirmation using longitudinal and sequencing-based studies is warranted.
Hybrid Time-Frequency ECG Arrhythmia Classification with Feature-Model Compatibility and Ensemble Decision Fusion J Prayoga; Melinda Melinda; Teuku Yuliar Arif; Herlina Dimiati
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1730

Abstract

Cardiac arrhythmia is a critical cardiovascular disorder associated with high global mortality, while electrocardiogram (ECG)-based diagnosis remains time-consuming and susceptible to inter-observer variability. Although recent artificial intelligence approaches have improved automated ECG analysis, many existing studies rely on single time-frequency representations and uniform classification strategies, limiting their ability to capture complementary ECG characteristics. This study proposed a hybrid time-frequency ECG arrhythmia classification framework incorporating feature–model compatibility and ensemble decision fusion to improve classification robustness and reliability. ECG signals from the MIT-BIH Arrhythmia Database were preprocessed using a fourth-order Butterworth bandpass filter and segmented using overlapping windows. To prevent data leakage, patient-wise cross-validation was employed, ensuring that ECG segments originating from the same patient were assigned exclusively to either training or testing folds. Three complementary time-frequency representations, namely Mel-Spectrogram, Short-Time Fourier Transform (STFT), and Discrete Wavelet Transform (DWT), were paired with their most compatible classifiers: Convolutional Neural Network (CNN); Random Forest (RF); and Support Vector Machine (SVM); respectively. Decision fusion was performed using stacking, hard voting, and soft voting strategies. Experimental results showed that the DWT-SVM model with stacking achieved the best overall performance, attaining 94.82% accuracy and a 94.59% F1-score, while STFT-RF with hard voting achieved comparable performance with 94.75% accuracy and a 94.50% F1-score. In contrast, Mel-Spectrogram-CNN produced substantially lower performance with 81.45% accuracy, indicating limited suitability of Mel-scale representations for ECG morphology analysis. Statistical analysis confirmed significant performance differences among models (p < 0.001). The findings demonstrate that integrating hybrid time-frequency representations with feature–model compatibility and model-dependent decision fusion provides a robust framework for automated ECG arrhythmia classification with strong potential for clinical decision support and real-time cardiac monitoring applications
Co-Authors Aslinar Aslinar J Prayoga Melinda Melinda Nur Indrawati Sofia Sofia Teuku Yuliar Arif