Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam
Dandy Prasetyo Nugroho
Universitas Dian Nuswantoro

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Integration of Ensemble Stacking in Machine Learning for Thermal Stability Prediction of Metal-Organic Frameworks (MOF) Ananta Surya Pratama; Muhammad Diva Irnanda; Taufiqul Umam; Dandy Prasetyo Nugroho; Harun Al Azies
SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam Vol. 14 No. 2 (2025): Volume 14 Nomor 2 (September 2025)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/mg9zs075

Abstract

Metal–Organic Frameworks (MOF) are porous materials with broad potential in energy, catalysis, and gas storage applications; however, their thermal stability remains a critical challenge. This study aims to develop a predictive model for the thermal stability of Zn-MOF based on the Quantitative Structure–Property Relationship (QSPR) using a stacking ensemble regression approach. The model was constructed with six base algorithms—Gaussian Process, KNN Regression, Ridge Regression, Linear Regression, Lasso Regression, and Support Vector Regression (SVR)—which were subsequently combined through TheilSen Regression as a meta-learner. The evaluation results demonstrate excellent predictive performance, with an MAE of 0.0022, an RMSE of 0.0023, and an R² of 0.9994 on the test set. Validation against experimental data also revealed only a tiny prediction error, confirming the model’s ability to accurately represent the complex relationships between structural descriptors and thermal stability. Compared to single regression models, the stacking approach proved to be more stable, robust against outliers, and adaptive to heterogeneous data distributions. These findings highlight the potential of stacking ensembles as an efficient data-driven material design strategy, enabling faster identification and design of thermally stable MOF while reducing reliance on costly and time-consuming laboratory experiments.
Co-Authors Ananta Surya Pratama Harun Al Azies Muhammad Diva Irnanda Taufiqul Umam