Smartphone cameras enable continuous, equipment-free heart rate monitoring, yet the photoplethysmography (PPG) signals they capture are susceptible to movement, contact instability, and environmental variation that differ sharply from controlled validation settings. This study evaluated heart rate estimation from smartphone PPG signals using a feature-based approach, five classical regression models, and Recursive Feature Elimination (RFE) on the BUT PPG v2.0.0 dataset (3,888 recordings from 50 subjects, ECG reference). A total of 21 features were extracted from time, morphology, frequency, and signal quality domains, normalized using Yeo-Johnson, and evaluated under a subject-wise 5-fold cross-validation scheme. SVR with an RBF kernel achieved the best performance (MAE 9.20 bpm, RMSE 12.05 bpm). Feature selection reduced the feature count from 21 to 12 with negligible performance loss (MAE 9.19 bpm), and a Jaccard Stability Index of 0.7513 indicated that the selected subset generalizes consistently. Condition-stratified error analysis revealed that signal quality is the primary error driver, with MAE rising from 6.49 bpm on clean signals to 9.91 bpm on noisy ones, and that dynamic activities such as walking (13.69 bpm), laughing (12.74 bpm), and coughing (11.62 bpm) produce the highest errors. These findings indicate that upstream signal quality assessment is a necessary component of a reliable smartphone PPG heart rate estimation system.
I. Azizah, Fatma Indriani, D. Nugrahadi et al.· 2026 International Conferenc...· 0 citations
Pneumonia remains one of the leading causes of morbidity and mortality worldwide, particularly among children, older adults, and immunocompromised individuals. Although chest X-ray (CXR) imaging is widely used for pneumonia diagnosis, manual interpretation is time-consuming, subjective, and highly dependent on radiologist expertise. Deep learning has shown promising performance for automated pneumonia classification; however, class imbalance remains a major challenge that can lead to biased predictions and reduced model generalization. Therefore, this study investigates the effectiveness of image augmentation and class weighting for handling class imbalance in pneumonia classification using chest X-ray images. The main contribution of this study is a systematic comparison of four experimental scenarios: Baseline, Augmentation Only, Class Weighting Only, and Hybrid (Image Augmentation and Class Weighting) implemented on a ResNet50 architecture integrated with the Convolutional Block Attention Module (CBAM). Experiments were conducted using the publicly available Chest X-Ray Images (Pneumonia) dataset from Kaggle, comprising 1,583 Normal and 4,273 Pneumonia images. Model performance was evaluated using Accuracy, ROC-AUC, Precision, Recall, F1-Score, confusion matrix analysis, and Youden’s J-Statistic for threshold optimization. The Hybrid model achieved the best overall performance, with an Accuracy of 95.74%, a ROC-AUC of 98.83%, a Macro Precision of 96.01%, a Macro Recall of 93.13%, and a Macro F1-Score of 94.44%. Moreover, the number of false negative predictions decreased from 25 in the Baseline model to 5 in the Hybrid model. These findings demonstrate that integrating image augmentation and class weighting within the ResNet50-CBAM framework effectively mitigates class imbalance and improves the reliability of automated pneumonia classification.
Kafilah Akhmad Fatahillah, T. H. Saragih, D. Kartini et al.· Indonesian Journal of Electr...· 0 citations
The rapid expansion of digital payments has produced massive volumes of user-generated reviews, making manual analysis impractical. This study focuses on the challenge of neutral sentiment classification in Indonesian e-wallet reviews, where neutral comments often contain ambiguous language and are underrepresented relative to positive and negative classes. A total of 26,537 preprocessed DANA application reviews were used to evaluate whether Word2Vec embeddings and Easy Data Augmentation (EDA) can improve neutral sentiment detection when combined with Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM) architectures. Experiments comparing eight model configurations showed that the combination of Word2Vec, EDA, and LSTM achieved the best performance, with 0.861 accuracy, 0.841 macro-F1, and 0.749 F1-score for the neutral class. These findings demonstrate that semantic representations and controlled lexical variation can jointly enhance minority-class recognition in short informal Indonesian text and highlight the importance of aligning embedding strategies with sequence architectures.
Muhammad Fattah Edric Camilo, Fatma Indriani, M. Faisal et al.· Jurnal Informatika· 0 citations
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