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Stacking Machine Learning Approach for Predicting Thermal Stability of Zinc–Metal Organic Frameworks (Zn-MOF) Pratama, Ananta Surya; Irnanda, Muhammad Diva; Umam, Taufiqul; Nugroho, Dandy Prasetyo; Azies, Harun Al
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8329

Thermal stability is a fundamental parameter that determines the feasibility of Metal Organic Frameworks (MOF) for high-temperature industrial applications, including catalysis, gas purification, and energy storage. Experimental evaluation of thermal stability, while accurate, is often costly and time-consuming, highlighting the need for computational prediction models that are both efficient and dependable. This study develops a Quantitative Structure Property Relationship (QSPR) model using a stacking ensemble regression framework to predict the thermal stability of Zn-MOFs. The stacking approach combines Linear Regression, Lasso Regression, and Huber Regression as base learners, with Linear Regression serving as the meta-model, thereby leveraging the complementary strengths of individual algorithms. Results demonstrate that the stacking ensemble consistently outperformed all single models, delivering highly reliable predictions that remained stable across multiple validation scenarios. Furthermore, external validation with experimental data confirmed the model’s robustness and its ability to generalize beyond the training dataset. These findings underline the reliability of stacking as not only a tool for improving accuracy but also for ensuring predictive stability and reproducibility. The study highlights the potential of machine learning, particularly ensemble methods, as a powerful and trustworthy predictive framework for the rational design of thermally stable MOFs, offering both scientific and industrial significance in sustainable energy applications.

Integration of Ensemble Stacking in Machine Learning for Thermal Stability Prediction of Metal-Organic Frameworks (MOF) Pratama, Ananta Surya; Umam, Taufiqul; Irnanda, Muhammad Diva; Nugroho, Dandy Prasetyo; Azies, Harun Al
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 14, No 2 (2025): September
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat142759682025

This study aims to develop a predictive model for the thermal stability of Zinc-based Metal-Organic Frameworks (Zn-MOFs), which are crucial in high-temperature applications. The approach used is stacking ensemble learning, which integrates several base models, including Ridge Regression, Lasso Regression, K-Nearest Neighbors (KNN) Regression, Support Vector Regression (SVR), Linear Regression, RANSAC (Random Sample Consensus), Huber Regression, and Gaussian Process Regression, with the meta-model TheilSenRegressor. Experimental results indicate that the stacking model delivers high-accuracy predictions, evidenced by a Root Mean Squared Error (RMSE) of 0.0025 and a coefficient of determination (R²) of 0.9993 on the training data, and an RMSE of 0.0023 and an R² of 0.9994 on the test data, demonstrating the model's excellent generalization capability. A comparison with the Robust Regression model shows that the stacking model is more stable and consistent in providing accurate predictions for both the training and test sets. These findings suggest that the machine learning-based stacking ensemble learning approach can serve as a more efficient and faster alternative to conventional experimental methods in predicting the thermal stability of Zn-MOFs.

Integration of Ensemble Stacking in Machine Learning for Thermal Stability Prediction of Metal-Organic Frameworks (MOF) Pratama, Ananta Surya; Umam, Taufiqul; Irnanda, Muhammad Diva; Nugroho, Dandy Prasetyo; Azies, Harun Al
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 14, No 2 (2025): September
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat142759682025

This study aims to develop a predictive model for the thermal stability of Zinc-based Metal-Organic Frameworks (Zn-MOFs), which are crucial in high-temperature applications. The approach used is stacking ensemble learning, which integrates several base models, including Ridge Regression, Lasso Regression, K-Nearest Neighbors (KNN) Regression, Support Vector Regression (SVR), Linear Regression, RANSAC (Random Sample Consensus), Huber Regression, and Gaussian Process Regression, with the meta-model TheilSenRegressor. Experimental results indicate that the stacking model delivers high-accuracy predictions, evidenced by a Root Mean Squared Error (RMSE) of 0.0025 and a coefficient of determination (R²) of 0.9993 on the training data, and an RMSE of 0.0023 and an R² of 0.9994 on the test data, demonstrating the model's excellent generalization capability. A comparison with the Robust Regression model shows that the stacking model is more stable and consistent in providing accurate predictions for both the training and test sets. These findings suggest that the machine learning-based stacking ensemble learning approach can serve as a more efficient and faster alternative to conventional experimental methods in predicting the thermal stability of Zn-MOFs.

Building an Intelligent System for Food Distribution Empowered by AI: Tackling Surplus–Deficit Inequality in the Barlingmascakeb Agglomeration Al Azies, Harun; Pratama, Ananta Surya; Umam, Taufiqul; Irnanda, Muhammad Diva
Jurnal Dinamika Ekonomi Pembangunan Vol 8 (2025): Special Issue: Call for Paper Pusaka Jateng
Publisher : Fakultas Ekonomika dan Bisnis, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jdep.8.0.19-40

Food distribution disparities remain a persistent challenge in the Barlingmascakeb region (Banyumas, Cilacap, Purbalingga, Banjarnegara, and Kebumen), where socio-economic and infrastructural factors drive regional inequalities. This study applies a machine learning–based classification approach to identify sub-districts categorised as food surplus or deficit. The dataset, initially imbalanced, was balanced using the Synthetic Minority Oversampling Technique (SMOTE), followed by training and evaluating four ensemble algorithms: AdaBoost, Gradient Boosting, XGBoost, and CatBoost. Among the tested models, AdaBoost demonstrated the best overall performance with an accuracy of 0.9565, precision of 1.00, recall of 0.8333, and F1-score of 0.9091. Gradient Boosting achieved a more balanced recall (0.8333) than XGBoost and CatBoost, although with lower precision. Based on the Gradient Boosting model, Feature importance analysis identified the Food Security Index as the most critical determinant of food status, followed by clean water access, morbidity rate, health workforce availability, and poverty levels. This study offers a novel contribution by providing a high-resolution, sub-district-level classification of food surplus and deficit conditions using interpretable ensemble machine learning models integrated with multidimensional socio-economic and health indicators. Practically, the model supports targeted and data-driven food distribution policies; theoretically, it reinforces the multifaceted nature of food security beyond production alone; and for future research, it opens opportunities to extend the framework to spatio-temporal and optimization-based food distribution models.

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