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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
A’isya Nur Aulia Yusuf
Universitas Jenderal Soedirman
Computer Science & IT

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Interpretable machine learning for digital soil pH mapping using an optimized AdaBoost algorithm Zakiyyan Zain Alkaf; A’isya Nur Aulia Yusuf; Elsa Sari Hayunah Nurdiniyah; Tri Wisudawati
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 4: August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i4.27724

Abstract

Soil pH is a fundamental parameter determining nutrient availability, microbial activity, and crop productivity. Unlike previous studies that often prioritize prediction accuracy over explainability, this study proposes an interpretable machine-learning framework integrating hyperparameter optimized adaptive boosting (AdaBoost) with Shapley Additive exPlanations (SHAP) to unravel the spatial drivers of soil pH. A systematic workflow was implemented to evaluate a diverse set of algorithms, followed by Bayesian optimization to fine-tune the best-performing models. The results demonstrated that the optimized AdaBoost model yielded the largest performance improvement (~7.5%), achieving excellent accuracy on independent test data with a coefficient of determination (R²) of 0.817 and a mean absolute error (MAE) of 0.293. Furthermore, SHAP analysis identified iron (Fe) and calcium carbonate (CaCO₃) as the most influential predictors, revealing that Fe exhibits a strong inverse relationship with pH, while CaCO₃ shows a positive association. This framework successfully balances high predictive accuracy with pedological interpretability, offering a robust tool for digital soil mapping and precision agriculture.
Classification of premature cardiac contractions based on RFECV and ensemble learning Elsa Sari Hayunah Nurdiniyah; A’isya Nur Aulia Yusuf; Norma Amalia; Widhiatmoko Herry Purnomo; Azizah Najda Hafizha
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 3: June 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i3.27584

Abstract

Premature cardiac contractions, including premature atrial contractions (PACs) and premature ventricular contractions (PVCs), are common arrhythmias that may increase the risk of cardiovascular complications when they occur frequently. Accurate classification of these events from electrocardiogram (ECG) signals remains challenging due to noise and signal variability. This study proposes a machine learning–based classification framework that combines recursive feature elimination with cross-validation for feature selection and an ensemble learning strategy to improve classification robustness. The approach was evaluated using the Massachusetts Institute of Technology – Beth Israel Hospital (MIT-BIH) Arrhythmia database and achieved high classification performance, with an accuracy of 95.34%, F1-score of 92.11%, and balanced precision and recall for PVC and PAC. In addition, SHapley Additive exPlanations (SHAP) were employed to identify the most influential features, enhancing model interpretability. The results demonstrate that the proposed framework provides a reliable and interpretable solution for distinguishing premature cardiac contractions, highlighting its potential application in clinical decision support systems.
Co-Authors Azizah Najda Hafizha Elsa Sari Hayunah Nurdiniyah Norma Amalia Tri Wisudawati Widhiatmoko Herry Purnomo Zakiyyan Zain Alkaf