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All Journal SAINSMAT JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam
Irnanda, Muhammad Diva
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Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Physics

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Application of Artificial Neural Network (MLP) for Multivariate Analysis of Stunting Causes in Indonesia Irnanda, Muhammad Diva; Pratama, Ananta Surya; Putra, Fawwaz Azhima; Sugiyanto, Sugiyanto
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10205

Abstract

Stunting is a major public health challenge in Indonesia, primarily caused by prolonged malnutrition and recurrent infections during the First 1,000 Days of Life. This study utilizes the Multi-Layer Perceptron (MLP) neural network model to predict stunting, offering a new dimension in the analysis of complex data and identification of patterns influencing stunting. With its capabilities, the MLP model provides higher precision in detecting contributing factors to stunting. The evaluation results of the model show RMSE of 0.7231, MAE of 3.0313, and an R² value of 0.9463. The Food Security Index (IKP), feature X9, had the highest feature importance, followed by X5 (Lack of Clean Water) and X1 (NCPR). This study presents a novel approach to predicting stunting percentages and offers more objective insights to support evidence-based and effective health policies aimed at reducing stunting prevalence in Indonesia.
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

Abstract

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

Abstract

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

Abstract

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.
Co-Authors Harun Al Azies Nugroho, Dandy Prasetyo Pratama, Ananta Surya Putra, Fawwaz Azhima Sugiyanto - Umam, Taufiqul