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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Community: Jurnal Pengabdian Pada Masyarakat
Amri, Sahrul
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Health Professions Languange, Linguistic, Communication & Media

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Ecoprint: Upaya Mengurangi Paparan Digital pada Anak Melalui Sekolah Perempuan Kreatif Batursari Trisnapradika, Gustina Alfa; Amri, Sahrul; Ardiansyah, Nibras Bahy; Rozada, Akfi
Community : Jurnal Pengabdian Pada Masyarakat Vol. 4 No. 2 (2024): Juli : Jurnal Pengabdian Pada Masyarakat
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/community.v4i2.543

Abstract

Golden Age is an era for children which is most crucial in building character in the future. However, nowadays many children are exposed to gadgets from an early age. The role of parents is very important to facilitate creative and positive activities to divert children's addiction to gadgets. Ecoprint is one of the activities that can be done with children. So, the service team held a service in the form of ecoprint training which was attended by women from Desa Batursari who are members of the Batursari Creative Women's School. As a result, participants took part in the training enthusiastically and produced good creations. The hope is that this activity can be an idea for playing with children at home.
Enhancing the Predictive Accuracy of Corrosion Inhibition Efficiency Using Gradient Boosting with Feature Engineering and Gaussian Mixture Model Amri, Sahrul; Akrom, Muhamad; Trisnapradika, Gustina Alfa
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11560

Abstract

Prediction The development of Quantitative structure property relationship (QSPR) models for predicting corrosion inhibition efficiency (IE) often faces challenges due to small datasets, which heightens the risk of overfitting and results in less reliable performance assessments. This research creates an entirely leakage-free modeling framework by combining per-fold preprocessing, augmentation of training-only data, and rigorous Leave-One-Out Cross-Validation (LOOCV). A set of 20 pyridazine derivatives was evaluated using 12 quantum-chemical descriptors, including HOMO, LUMO, ΔE, dipole moment, electronegativity, hardness, softness, and the electron-transfer fraction. An initial assessment showed that all baseline models lacking augmentation Gradient Boosting, Random Forest, SVR, and XGBoost demonstrated limited predictive power (R² < 0.20), revealing the dataset's inherently low information complexity.To enhance representation in the feature space, a multi-scale Gaussian Mixture Model (GMM) was used to generate chemically valid synthetic samples, with all components trained solely on the training subset from each LOOCV fold. This strategy consistently improved model performance. The two most successful configurations, XGBoost + GMM v2 and Random Forest + GMM v3, reached R² values of 0.4457 and 0.4108, respectively, along with significant decreases in RMSE, MAE, and MAPE. These findings illustrate that GMM-based generative augmentation effectively captures multicluster structures within the descriptor space while expanding the chemical variability domain in a controlled way.While the resulting R² values remain inadequate for high-precision quantitative predictions, the proposed methodology provides a solid basis for early-stage evaluation of corrosion inhibitors in situations with limited data. Future research will aim to integrate advanced DFT-derived descriptors, molecular graph representations, and tests against larger external datasets to enhance model generalizability.
Co-Authors Akrom, Muhamad Ardiansyah, Nibras Bahy Rozada, Akfi Trisnapradika, Gustina Alfa