M. Faisal

Feature Selection and Performance Analysis for Heart Rate Estimation from Smartphone Photoplethysmography

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.

TabKANet for Software Defect Prediction: An Oversampling and Feature-Selection Ablation Study

Improving Neutral Sentiment Classification in Indonesian E-Wallet Reviews Using Word2Vec and Easy Data Augmentation (EDA)

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.

Comparative Evaluation of TabKANet with Oversampling and Feature Selection Ablation for Software Defect Prediction

TabKANet is a competitive architecture for all-numerical, highly imbalanced SDP, matching strong neural baselines and surpassing TabNet, where effective class weighting alone suffices and SMOTE is counter-productive.