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All Journal Saqbe : Jurnal Sains dan Pembelajarannya Jurnal Elektronika dan Telekomunikasi
Indra Maulana
Institut Prima Bangsa
Education Other

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Comparative Performance of Regression and Ensemble Learning Algorithms in Precision Irrigation Forecasting of Sweet Potato Muthia Rahmah; Indra Maulana
Jurnal Elektronika dan Telekomunikasi Vol. 25 No. 2 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.799

Abstract

Precision irrigation is essential for sustainable agriculture under increasing water scarcity. This study compared regression and ensemble learning algorithms for forecasting irrigation requirements in sweet potato, a crop characterized by high variability in water demand. An Internet of Things (IoT)-based prototype was deployed to collect real-time data on soil moisture, temperature, humidity, light intensity, and atmospheric pressure over 42 hours and 50 minutes (August 4-5, 2025), encompassing two complete diurnal cycles at 10-minute intervals and yielding 243 temporal observations. Following preprocessing and feature engineering with lag-based temporal features, the final dataset comprised 240 samples (192 training, 48 testing) using chronological time-based splitting to prevent data leakage. Five algorithms, Support Vector Regression (SVR), AdaBoost, Extreme Gradient Boosting (XGBoost), Random Forest Regressor (RFR), and CatBoost, were evaluated under default and hyperparameter-tuned configurations using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²) as evaluation metrics. Tuned Random Forest achieved superior performance (R² = 0.9802, RMSE = 9.58, MAE = 6.08), followed by default Random Forest (R² = 0.9786) and default CatBoost (R² = 0.9687). XGBoost demonstrated strong performance (R² = 0.9670 tuned) but exhibited overfitting tendencies with near-perfect training scores. SVR improved substantially after tuning (R² = 0.328 to 0.797), although it remained inferior to ensemble methods. Overall, ensemble methods, particularly XGBoost and Random Forest, demonstrated superior efficacy for sweet potato irrigation forecasting. These findings underscore the potential of IoT-integrated machine learning to enhance water-use efficiency and support sustainable smart farming practices.
Validation of Pre-Service STEM Teachers’ Acceptance and Use of Generative Artificial Intelligence Scale: Rasch Model Elsima Nainggolan; Saraswathy A/p Ramasundrum; Indra Maulana
Saqbe: Jurnal Sains dan Pembelajarannya Vol. 2 No. 1 (2025): Saqbe : Sains dan Pembelajarannya (Maret 2025)
Publisher : Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Generation Z pre-service STEM teachers, recognized as digital natives, possess strong potential to adopt Generative Artificial Intelligence (GAI) in educational contexts and advance its meaningful integration. Accordingly, this study aims to validate an instrument designed to measure their acceptance of and use of GAI. A quantitative cross-sectional survey was administered to 401 pre-service STEM teachers using a UTAUT2–TPB–based instrument. Data were collected via Google Forms and analyzed using Rasch modeling (Winsteps 3.7.3). The results confirm that the instrument possesses strong psychometric properties under the Rasch model. Analysis demonstrated high person (0.94) and item (0.97) reliabilities, well-defined separation indices, and acceptable item fit values, indicating that the scale effectively differentiates respondents and maintains stability across items. The unidimensionality test further supported that the instrument measures a single, coherent construct, reinforcing its internal structural integrity. Overall, these findings verify that the instrument is valid and reliable for assessing GAI acceptance and use among pre-service STEM teachers. The study offers both theoretical and practical contributions by providing a rigorously tested measurement tool to evaluate readiness for GAI adoption and integration in teacher education.
Co-Authors Elsima Nainggolan Muthia Rahmah Saraswathy A/p Ramasundrum